A perfect key to success by these 00M-229 practice questions

If you really to show your professionalism so just Passing the 00M-229 exam is not sufficient. You should have enough IBM SPSS Predictive Analytics Sales Mastery v1 knowledge that will help you work in real world scenarios. Killexams.com specially focus to improve your knowledge about 00M-229 objectives so that you not only pass the exam, but really get ready to work in practical environment as a professional.

Exam Code: 00M-229 Practice exam 2022 by Killexams.com team
IBM SPSS Predictive Analytics Sales Mastery v1
IBM Predictive Questions and Answers
Killexams : IBM Predictive Braindumps - BingNews https://killexams.com/pass4sure/exam-detail/00M-229 Search results Killexams : IBM Predictive Braindumps - BingNews https://killexams.com/pass4sure/exam-detail/00M-229 https://killexams.com/exam_list/IBM Killexams : Machine Learning and Artificial Intelligence in the Enterprise

Predictive analytics / machine learning / artificial intelligence is a hot Topic - what's it about?

Using algorithms to help make better decisions has been the "next big thing in analytics" for over 25 years. It has been used in key areas such as fraud the entire time. But it's now become a full-throated mainstream business meme that features in every enterprise software keynote - although the industry is battling with what to call it.

It appears that terms like Data Mining, Predictive Analytics, and Advanced Analytics are considered too geeky or old for industry marketers and headline writers. The term Cognitive Computing seemed to be poised to win, but IBM's strong association with the term may have backfired - journalists and analysts want to use language that is independent of any particular company. Currently, the growing consensus seems to be to use Machine Learning when talking about the technology and Artificial Intelligence when talking about the business uses.

Whatever we call it, it's generally proposed in two different forms: either as an extension to existing platforms for data analysts; or as new embedded functionality in diverse business applications such as sales lead scoring, marketing optimization, sorting HR resumes, or financial invoice matching.

Why is it taking off now, and what's changing?

Artificial intelligence is now taking off because there's a lot more data available and affordable, powerful systems to crunch through it all. It's also much easier to get access to powerful algorithm-based software in the form of open-source products or embedded as a service in enterprise platforms.

Organizations today have also more comfortable with manipulating business data, with a new generation of business analysts aspiring to become "citizen data scientists." Enterprises can take their traditional analytics to the next level using these new tools.

However, we're now at the "Peak of Inflated Expectations" for these technologies according to Gartner's Hype Cycle - we will soon see articles pushing back on the more exaggerated claims. Over the next few years, we will find out the limitations of these technologies even as they start bringing real-world benefits.

What are the longer-term implications?

First, easier-to-use predictive analytics engines are blurring the gap between "everyday analytics" and the data science team. A "factory" approach to creating, deploying, and maintaining predictive models means data scientists can have greater impact. And sophisticated business users can now access some the power of these algorithms without having to become data scientists themselves.

Second, every business application will include some predictive functionality, automating any areas where there are "repeatable decisions." It is hard to think of a business process that could not be improved in this way, with big implications in terms of both efficiency and white-collar employment.

Third, applications will use these algorithms on themselves to create "self-improving" platforms that get easier to use and more powerful over time (akin to how each new semi-autonomous-driving Tesla car can learn something new and pass it onto the rest of the fleet).

Fourth, over time, business processes, applications, and workflows may have to be rethought. If algorithms are available as a core part of business platforms, we can provide people with new paths through typical business questions such as "What's happening now? What do I need to know? What do you recommend? What should I always do? What can I expect to happen? What can I avoid? What do I need to do right now?"

Fifth, implementing all the above will involve deep and worrying moral questions in terms of data privacy and allowing algorithms to make decisions that affect people and society. There will undoubtedly be many scandals and missteps before the right rules and practices are in place.

What first steps should companies be taking in this area?

As usual, the barriers to business benefit are more likely to be cultural than technical.

Above all, organizations need to make sure they have the right technical expertise to be able to navigate the confusion of new vendors offers, the right business knowledge to know where best to apply them, and the awareness that their technology choices may have unforeseen moral implications.

[This article originally appeared on the Business Analytics and Digital Analytics Blog]

Sun, 26 Jun 2022 08:24:00 -0500 en text/html https://www.zdnet.com/paid-content/article/key-questions-on-machine-learning-and-artificial-intelligence-in-the-enterprise/
Killexams : Prescriptive & Predictive Analytics Market Analysis, Strategic Assessment, Trend Outlook and Business Opportunities 2022-2028

The Prescriptive & Predictive Analytics Market Size was valued at US$ 13.74 bn in 2021 and is predicted to reach at US$ 46.46 Bn by 2028, with a healthy CAGR of 19.76%.

This press release was orginally distributed by SBWire

Pune, Maharashtra — (SBWIRE) — 07/14/2022 — The emergence of COVID-19 has slowed down the market growth, but due to uplifting lockdowns, the market is slowly gaining traction. The report offers a deep topographical analysis for key regions and country markets. Increasingly, prescriptive analytics is becoming embedded in business applications.

Predictive analytics offers companies actionable insights based on data. It delivers estimates about the likelihood of a future outcome, which can be used to get a forecast of future developments. Moreover, the foundation of predictive analytics is based on probabilities. Prescriptive analytics involves using techniques such as business rules and machine learning to process large data sets. This kind of processing is typically applied against historical and transactional data as well as real-time data feeds. The businesses that have already adopted some sort of descriptive analytics tools and solutions are better positioned for predictive and /or prescriptive analytics solutions adoption.

Get a trial Report of Prescriptive & Predictive Analytics Market (With Detailed TOC, Tables, Regional Analysis, Graphs & Charts) @ https://www.snsinsider.com/sample-request/2313

for more information or customization mail us at [email protected]

Major Company Profiles included in Prescriptive & Predictive Analytics Market are:

– Oracle Corporation
– SAP SE
– IBM Corporation
– Microsoft Corporation
– SAS Institute Inc.
– Altair Engineering, Inc
– ALTERYX, INC
– FICO
– Teradata
– TIBCO Software Inc

This study report's main focus is COVID-19, which gives a deep and in-depth analysis of how the epidemic has driven this industry to change. COVID-19's current and projected market outcomes, as well as a modern viewpoint on the ever-changing commercial zone, are examined and analyzed in this research. It also includes vital information such as historical growth analysis, CAGR status, price structure, and the supply-demand climate in the market. This research paper examines the supply chain, import and export controls, regional government policy, and the sector's potential impact in the aftermath of the global COVID-19 pandemic. The research can assist players in gaining a better understanding of the Prescriptive & Predictive Analytics market and developing appropriate company expansion plans.

Everything from marketing channels and market positioning to future growth strategies for new entrants and established competitors in the sector is included in the strategy analysis. In terms of growth rate, market segmentation, market size, future trends, and geographical viewpoint, the research report comprises both qualitative and quantitative data. The study examines the Prescriptive & Predictive Analytics market's current prognosis, which is expected to have an impact on its future potential. The effects of major company product dynamics, industry development trends, regional industrial layout characteristics, macroeconomic policies, and industrial policy have all been considered. Raw materials to end users, as well as trends in product circulation and sales channel, will be thoroughly explored in this business.

Major Segments and Sub-Segment of Prescriptive & Predictive Analytics Market are Listed Below:

On The Basis of Deployment:

– On-Premise
– Cloud

On The Basis of Industry:
– BFSI
– Retail
– Healthcare And Pharmaceutical
– It And Telecom
– Others

Enquiry about this report @ https://www.snsinsider.com/enquiry/2313

(Do you have any specific query regarding this research? Let's talk to our market experts to understand better view of market status.)

Regional Coverage:

-North America [United States, Canada]
-Europe [Germany, France, U.K., Italy, Russia]
-Asia-Pacific [China, Japan, South Korea, India, Australia, China Taiwan, Indonesia, Thailand, Malaysia]
-Latin America [Mexico, Brazil, Argentina]
-Middle East & Africa [Turkey, Saudi Arabia, UAE]

Research Methodology

Using both primary and secondary research approaches, we looked at the Prescriptive & Predictive Analytics market from a variety of angles. This allowed us to better comprehend current market dynamics like supply-demand imbalances, pricing trends, product preferences, and customer behavior patterns, to mention a few.

Competitive Scenario

The report is organized in each of the study's areas and nations to include both qualitative and quantitative industry characteristics. The research also includes an in-depth examination of critical areas such as driving forces and barriers that will shape the market's future development. The research will also include a thorough evaluation of the competitive landscape and large firms' product offerings, as well as micro market investment potential for stakeholders. The goal of the Prescriptive & Predictive Analytics market study is to estimate market sizes for various industries and areas in previous years in order to project market sizes for the following eight years.

Report Conclusion

The Prescriptive & Predictive Analytics market study will aid market participants in identifying important market opportunities and building strategies to get a competitive advantage in the global market.

Frequently asked Questions in Prescriptive & Predictive Analytics market report are:

– Which region has largest share in Predictive and Prescriptive Analytics Market?
– Who are the key players in Predictive and Prescriptive Analytics Market?
– What is the growth rate of Predictive and Prescriptive Analytics Market?

Table of Contents – Major Key Points

1. Introduction

2. Research Methodology

3. Market Dynamics

4. Impact Analysis

5. Value Chain Analysis

6. Porter's 5 Forces Model

7. PEST Analysis

8. Prescriptive & Predictive Analytics Market Segmentation, by Deployment

9. Prescriptive & Predictive Analytics Market Segmentation, by Industry

10. Regional Analysis

11. Company Profiles

12. Competitive Landscape

13. Conclusion

Buy Single User PDF of Prescriptive & Predictive Analytics Market @ https://www.snsinsider.com/checkout/2313

About Us:

SNS Insider is a market research and insights firm that has won several awards and earned a solid reputation for service and strategy. We are not merely a research organization. We are a strategic partner who can assist you in reframing issues and generating answers to the trickiest business difficulties. For greater consumer insight and client experiences, we leverage the power of experience and people.

When you employ our services, you will collaborate with qualified and experienced staff. We believe it is crucial to collaborate with our clients to ensure that each project is customized to meet their demands. Nobody knows your customers or community better than you do. Therefore, our team needs to ask the correct questions that appeal to your audience in order to collect the best information.

For more information on this press release visit: http://www.sbwire.com/press-releases/prescriptive-predictive-analytics-market-analysis-strategic-assessment-trend-outlook-and-business-opportunities-2022-2028-1360673.htm

Thu, 14 Jul 2022 08:03:00 -0500 ReleaseWire en-US text/html https://www.digitaljournal.com/pr/prescriptive-predictive-analytics-market-analysis-strategic-assessment-trend-outlook-and-business-opportunities-2022-2028
Killexams : What You Leave Behind While Shopping Online

“Big Data Saves...”

Go ahead. Fill in the blank. Put in anything you want. Because regardless of what you choose, there’s likely to be a news article supporting your claim. This is especially true when it comes to retail. If headlines are to be believed, then big data has saved the British pubs, it’s pumped life back into the practice of in-store promotions, and it’s brought Ford Motors (and, therefore, all of Detroit) back from the dead.

There is only one problem with big data. It’s so mind-bogglingly big. And it keeps getting bigger all the time. Public archives are getting loaded online. New forms of social media are constantly emerging and old ones continue to recruit new users. Satellites observe and report minute changes in weather patterns while cameras here on Earth seem to capture every newsworthy thing we do.

Retailers, like everyone, are awash in big data. But it will only save them if they can find a way to make the data relevant to the specific businesses they are running.

“Retailers are trying to get smarter by anticipating their customers' needs,” says Karen Lowe, the global general manager for IBM’s retail industry. “This requires predictive analytics. They are having to tap into external sources of data. And that's not always easy.”

Lowe leads an effort to take the extraordinary amounts of data that IBM collects and package it into analytical tools that can be easily customized by the client. The products offered through IBM’s Smart Retail Solutions services are designed to help retailers make decisions about their own individual businesses while filtering out the noise that inevitably accompanies large datasets.

Part of the strategy involves observing what people are saying on social media outlets and using that to gauge customer sentiment about particular brands. This may sound easy. After all, most people don’t exactly hide their true feelings from the audiences of Twitter and Facebook. But then again, one person’s incensed review of a product, however passionately it was written, may not reflect the average customer’s opinion. Similarly, even a brief, understated comment can have serious consequences for a company if the person who made it is influential in the digital community.

“Can tweets about your brand, especially from a social influencer, actually change sales?” asks Lowe. The challenge is to answer only this question, sifting thru the noise. Doing so requires a system that understands all the connections and reputations in a given social network. “Knowing who’s influencing who and what they’re saying is becoming more and more important over time," she said.

But making data relevant to specific clients isn’t always a matter of filtering out the noise. Sometimes it requires actually hunting down unconventional sources of data that can offer some rare insight into your specific business.

.

The Rise of E-Commerce

Online sales are growing steadily year over year, showing no signs of slowing down.

.

E-Commerce Growth Outpaces Retail

The number of online retail outlets is growing dramatically as brick-and-mortar stores drop slightly.

.

The Brick-and-Mortar Data Gap Part I

Brick-and-mortar retailers struggle to translate the personalized online shopping experience to their stores…

.

The Brick-and-Mortar Data Gap Part II

…even as the industry recognizes how it can help their business.

.

The Brick-and-Mortar Data Gap Part III

But in exact years, retailers have made strides to catch up with big data adoption across all industries.

.

The Brick-and-Mortar Data Gap Part IV

Still, for the time being, retailers lag behind other industries when it comes to taking realizing the value of big data.

.

Share This

 

According to Vish Ganapathy, IBM’s chief retail technologist, IBM helps its clients find significance in datasets that would not normally pertain to them.

“One of our clients, which is an automobile parts retailer—they did something very, very clever,” says Ganapathy. “They took department of motor vehicles data to figure out who was living in what area, what kind of cars, what model, what make, et cetera. And used that to influence the assortments in their store. It’s very, very clever, extremely relevant to their business, and we are seeing lots of anecdotal examples like that.”

One of the most relevant measurements business owners can have is an indication of how their shops are faring in comparison to all others like them. Brick-and-mortar retailers have the benefit of being able to look out the shop window and see where the crowds are forming. Performance comparison is especially challenging for online businesses that see their own analytics in real time, but rarely get a glimpse at the competition's.

Then again, even if you could inspect the innards of every individual e-commerce company, it wouldn’t necessarily be helpful as only a sliver of them would be relevant to your business profile.

Through agreements with online retailers, IBM has access to performance data in real time, and it uses it to make comparative analyses of how its retail clients are doing in their specific sector of the market. For example, a department store can log in and look at how their competitors are performing in every category of sales, whether it be women’s apparel or home goods.

“And so they can see—what’s going on with me? What are they saying about my competitors? Why am I getting less traffic right now than my peers in the marketplace?” says Lowe. “Analytics that converts data into actionable insight...these are the analytics that are happening right now."

But most importantly, they are analytics that pertain only to them—insights pulled from the tsunami of big data and delivered to companies as life vests tailored only to them.

NEXT: What Will Replace Today’s Banks?

Tue, 23 Dec 2014 22:55:00 -0600 text/html https://www.theatlantic.com/sponsored/ibm-transformation/what-you-leave-behind-while-shopping-online/245/
Killexams : Operational Predictive Maintenance Market Trends, Key Players, Sales, Supply, Demand, Analysis & Forecast to 2028

The MarketWatch News Department was not involved in the creation of this content.

Jul 14, 2022 (Market Insight Reports) -- New Analysis Of Operational Predictive Maintenance Market overview, spend analysis, imports, segmentation, key players, and opportunity analysis 2022-2028. The report offers an up-to-date analysis of the current global Operational Predictive Maintenance market scenario, the latest trends and drivers, and the overall market environment. The market is driven by Operational Predictive Maintenance growth worldwide. In addition, the study used an objective combination of primary and secondary information including inputs from key participants in the industry. the report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors. The study also includes an in-depth competitive analysis of the key market players, along with their company profiles, key observations related to product and business offerings, exact developments, and key market strategies.

Our Experts will help you get valuable insights about Operational Predictive Maintenance market share, size, and regional growth prospects. Available Other Related Market Research Reports

Sample PDF Report at-https://reportsinsights.com/sample/590788

Top Key Market Players for Operational Predictive Maintenance Market Listed are:- IBM Corporation, Software AG, SAS Institute, PTC, General Electric, Robert Bosch, Rockwell Automation, Schneider Electric, eMaint

The Operational Predictive Maintenance Market report also covers the current competitive scenario and the predicted trend; and profiles key vendors including market leaders and important emerging players. It also provides a snapshot of the country's economy and Industry outlook. It provides details on the current Situation for Operational Predictive Maintenance manufacturing in the United States, Europe, and the Asia Pacific including - policy initiatives, joint ventures, current requirements, and locational attractiveness. Apart from this, the valuable document weighs upon the performance of the industry on the basis of a product service, end-use, geography, and end customer.

The market research includes historical and forecast market data, demand, application details, price trends, and company shares of the leading Operational Predictive Maintenance by geography. The report splits the market size, by volume and value, on the basis of application type and geography.

Operational Predictive Maintenance Market segment by Type, covers are:
Software

Services

Operational Predictive Maintenance Market segment by Application can be divided into:
Public Sector

Automotive

Manufacturing

Healthcare

Energy & Utility

Transportation

Regional Operational Predictive Maintenance Market (Regional Output, Demand & Forecast by Countries):-
North America (United States, Canada, Mexico)
South America ( Brazil, Argentina, Ecuador, Chile)
Asia Pacific (China, Japan, India, Korea)
Europe (Germany, UK, France, Italy)
Middle East Africa (Egypt, Turkey, Saudi Arabia, Iran) And More.

Access full Report Description, TOC, Table of figures, Chart, etc. at-https://www.reportsinsights.com/industry-forecast/operational-predictive-maintenance-markets-growth-trends-590788

The report is useful in providing answers to several critical questions that are important for the industry stakeholders such as manufacturers and partners, end-users, etc., besides allowing them in strategizing investments and capitalizing on market opportunities.

Reasons to Buy This Report

– The new players in the Global Operational Predictive Maintenance Market and the potential entrants into this market can use this report to understand the key market trends that are expected to shape the market in the next few years.
– The Market Analysis Chapter covers the Key Drivers, Restraints, and Challenges of the Operational Predictive Maintenance Market. The PESTLE and Porters five forces are covered in detail in this report.
– The key technologies that could impact the Global Operational Predictive Maintenance Market have been covered in detail.
– The report can be used by the sales and marketing team to formulate their medium- and long-term strategies and to reconfirm their short-term plans.
– The report would help the sales and the marketing team to understand the key segments across the top fifteen countries which have been analyzed in the report.
– The Opportunity Analysis chapter identifies the key hot spots within the Global Operational Predictive Maintenance Market.
– The company profiles include financials, the latest news, and a SWOT analysis for ten companies.

Besides, the market study affirms the leading players worldwide in the Global Operational Predictive Maintenance market. Their key marketing strategies and advertising techniques have been highlighted to offer a clear understanding of the Global Operational Predictive Maintenance market.

About reports insights:

Reports Insights is the leading research industry that offers contextual and data-centric research services to its customers across the globe. The firm assists its clients to strategize business policies and accomplish sustainable growth in their respective market domains. The industry provides consulting services, syndicated research reports, and customized research reports.

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COMTEX_410129799/2599/2022-07-14T05:21:21

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Wed, 13 Jul 2022 17:21:00 -0500 en-US text/html https://www.marketwatch.com/press-release/operational-predictive-maintenance-market-trends-key-players-sales-supply-demand-analysis-forecast-to-2028-2022-07-14
Killexams : Products and Resources

Software enables Industrial IoT

Enhancements across the company’s software portfolio make it possible to operate plants, analyze industrial data, and optimize operations. Predix Essentials, a SaaS solution, helps companies connect to disparate data sources, monitor operations, and use edge-to-cloud predictive analytics. Developed in partnership with customers including Intel, Predix Essentials is a first step toward using cloud-based Industrial Internet of Things (IIoT) technologies for digital transformation. It is also the foundation of the company’s APM and OPM application suites. Asset Answers is a benchmarking tool that lets users import and assess data to compare their asset maintenance practices with similar companies or their own performance across sites. Webspace 6.0 brings the visualization and control capabilities of the company’s iFIX and CIMPLICITY HMI/SCADA software to mobile devices. With encryption and a zero-install HTML5 client, Webspace 6.0 helps Strengthen how operators receive and react to operational insights.
GE
www.ge.com/digital

Stream industrial data into MES

The DataHub IoT Gateway streams real-time OPC UA and OPC DA industrial data directly into manufacturing execution systems (MESs), device clouds, and big data analytics platforms. The gateway connects OPC UA and OPC DA (Classic) clients and servers to any MQTT broker, including Azure, Google, and Amazon IoT. It supports both publish and subscribe and automatic OPC to MQTT protocol conversion, maintaining the OPC UA data model while other gateways flatten it. The gateway lets you merge data from multiple sources into a common data set, configure a network of DataHub installations from a single location, quickly view live trends for selected data, and control access and set permissions for users and groups. Add-on options include the ability to build and display private cloud-based web pages, connect two or more data sources to share data in real time, connect OPC A&E servers and clients, connect Modbus TCP slave devices, and read/write data to any ODBC database.
Cogent
cogentdatahub.com/products/datahub-iot-gateway/

Wireless access point combines Wi-Fi, WirelessHART

Specifically engineered to meet the smaller network requirements of remote operations, the Wireless 1410 Gateway is also secure and flexible. This compact wireless access point connects WirelessHART networks with host systems and data applications, and its small size and DIN-rail mount capability make it suitable for limited cabinet space. The device has two network capacity options (A: 25 devices / B: 100 devices) to meet various network demands. To help with digital transformation, this industrial networking approach combines the company’s expertise in industrial automation and applications with Cisco’s innovations in networking, cybersecurity, and information technology infrastructure. This wireless access point provides the Wi-Fi bandwidth necessary for real-time safety monitoring, including location awareness and wireless video—applications intended to enhance personnel safety practices, Strengthen plant security, and help ensure environmental compliance.
Emerson
www.emerson.com/en-us/catalog/emerson-sku-1410-wireless-gateway 

Real-time edge intelligence for IIoT

The Lightning Edge AI platform, which enables real-time edge intelligence via the IIoT, includes tools and enhancements for operational technology (OT) professionals. The drag-and-drop analytic programming capabilities and visualization dashboards help OT staff derive insights from real-time data without assistance from data science teams. The platform brings intelligence to or near the point where data originates and facilitates analysis with the lowest latencies to Strengthen operational outcomes. Artificial intelligence (AI) is enabled through built-in closed-loop, edge-to-cloud machine learning, where the system can detect drifts in model accuracies and automatically trigger cloud-based retraining with Google Cloud Platform and Microsoft Azure IoT and republish new models to the edge in an iterative fashion until the expected accuracy is reached.

This latest release includes a visual programming tool, VEL Studio, that creates analytic expressions that derive actionable insights from streaming control and sensor data. A drag-and-drop library of more than 100 built-in code blocks lets OT professionals perform traditional data science tasks without the need for any programming skills. OT-centric blocks for manufacturing-specific use cases create analytics including anomaly and failure condition detection. VIZ Dashboards allows OT teams to visualize real-time data streams and monitor the efficiency and health of their environments. Data ingestion agents include OPC-DA.
Foghorn
www.foghorn.io 

End-to-end security for Industrial IoT

SmartServer IoT is an open, end-to-end, extensible edge server that securely delivers operating system data to new cloud services. It enables system integrators, application developers, and original equipment manufacturers to deliver IIoT solutions for energy management and automation using both new and existing control networks. Using the IBM Watson IoT Platform enabled by NXP’s A71CH secure element for IoT devices, the server is an extra layer of security for businesses connecting to the IBM Cloud. The IBM Watson IoT Platform is a managed, cloud-hosted service. The SmartServer IoT simplifies interoperability between diverse legacy systems, disparate devices, and emerging and traditional protocols.

It provides built-in device and data management for sensors, meters, actuators, and controllers through a range of protocols, including BACnet, LonWorks, and Modbus. The NXP A71CH Plug & Trust Secure Element has X.509 certificates and keys trusted by Watson IoT Platform and injected at NXP secure certified facilities. NXP’s trust provisioning service ensures keys are kept safe, and credentials are injected in a trusted environment. When embedded into devices, the chips have the necessary keys to establish a secure TLS connection with IBM Watson IoT for seamless device-to-cloud connections.
Adesto
www.adestotech.com/smart-server-iot/ 

IIoT HMIs and controllers

The cMT series with CODESYS integrates high-performance HMI with a CODESYS programmable logic controller system on an architecture where a duo-core CPU runs two independent operating systems. With the multicore processor, cMT HMI + CODESYS provides data visualization with an operable user interface and also runs controller logic. The two systems run independently without mutual interference. The cMT-CTRL01 IIoT programmable logic controller has built-in CODESYS in addition to working with all iR modules. It has IIoT gateway protocol translation and EasyAccess remote access service. The cMT-G01/G02/G03/G04 is a smart communication gateway with the data processing capability of an HMI to facilitate IIoT integration. With OPC UA built-in, the gateway fits well into lots of applications to provide a standard communication interface and to integrate data.
Weintek USA
www.weintekusa.com/ 

Four-channel SFP Gigabit Ethernet card

The XMC477RC four-channel SFP Gigabit Ethernet interface supports four small form-factor pluggable (SFP) modules, allowing users to choose between 1000BASE-X optical or 1000BASE-T copper connectivity. When paired with the company’s single-board computer, the XMC477RC reportedly delivers functional density that can reduce the number of cards in a chassis. This can, for example, eliminate the requirement for a dedicated Ethernet switch and help minimize the size, weight, and power of a subsystem.

Able to operate in temperatures between –40°C to +85°C, the XMC477RC is suitable for naval/marine, land, and air platforms including fire control and radar systems. It supports a high-speed link to the host via an x4 PCIe connection, allowing all front I/O ports to run at full line rate. It has an industry-standard Intel I350-AM4 quad port Gigabit Ethernet controller that gives native support for enhanced virtualization elements, such as VMDq, and up to eight virtual machines allocated per port. Native drivers for common operating systems include Microsoft Windows, Linux, and LynxOS. VxWorks and Solaris drivers also available.
Abaco Systems
www.abaco.com/ 

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Killexams : What is AIops?

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.


Deploying software to support the work of an enterprise is an increasingly complex job that’s often referred to as ‘devops.’ When enterprise teams started using artificial intelligence (AI) algorithms to more efficiently and collaboratively run these operations, end users coined the term AIops for these tasks. 

AI can help large software installations by watching the software run and flag any anomalies or instances of poor performance. The software can examine logs and track key metrics, like response time, to evaluate the speed and effectiveness of the code. When the values deviate, the AI can suggest solutions and even implement some of them. 

There are several stages to the process:

  • Detection or observability: The software absorbs as many metrics and event logs as possible. The focus is generally on poor performance that can affect users directly, like a 404 error or an especially long database query run time. Some systems, though, may watch for other issues like a failed sensor or an overheated device. 
  • Predictive analytics: After collecting data for some time, AIops software can begin to identify precursors that can often signal an upcoming failure. The AI algorithms are optimized to look for correlations between values, especially those that are anomalies that may indicate upcoming problems. 
  • Proactive mitigation: Some AIops algorithms can be tuned to respond immediately to potential problems when the solution is straightforward. For example, a crashing service may be rebooted or reinitialized with more RAM. When these solutions work, they can eliminate much of the problem and save end users from encountering failures. 

AIops is growing in complexity as teams deploy algorithms to a variety of enterprises. One of the most valuable opportunities comes when organizations start to use other AI algorithms in daily operations. In these cases, AIops can help with deploying AI.  This way, there can be synergy between the software layers. 

Sometimes AIops teams use other subterms for their work. MLops, for example, deals specifically with using and deploying machine learning algorithms. DataOps can refer to the general problem of collecting data or the more specific problem of organizing the data that’s used to train and refresh an artificial intelligence model. 

Also read: MLops vs. devops: Why data makes it different

How can AIops support deployment of AI?

When AI scientists began to explore the best algorithms for AI, they worked with experimental computers in their labs. Now that AI is becoming regularly deployed in production environments, some are beginning to specialize in maintaining and running software. 

The challenges of supplying services with AI algorithms are the same as maintaining regular software. There should be sufficient computational power to answer all requests, even those that arrive together in a moment of peak demand. There should be systems in place to deliver the right versions of the software to the front-line hardware. When developers and scientists make changes, there should be a mechanism for testing them and eventually replacing the software on the front-line machine with the existing version. 

While much of the work is no different from standard devops. However, there are also concerns that are particular to AI and machine learning (ML). Some of these include:

  • The model is like another piece of software with its own version number and history. The AIops team will juggle models, often independently of the software itself. 
  • Training the model is often a time-consuming process that often requires an elaborate build process of its own. 
  • There are now different chips that are optimized in different ways for creating the model and running the model in production. AIops teams must plan the best available hardware for each task independently. 
  • The build process may involve much more experimentation than typical software development. It’s not uncommon for AIops teams to try different arrangements for neural networks and then evaluate how they perform. 
  • AIops teams may also have a third job of tracking the datasets that are used for training and evaluation. These datasets may also evolve with their own version numbers and history. 
  • Some applications deliberately feed data back into the training set over time, so the set grows and the results improve. AIops teams must also maintain the evolution of the training data over time. 
  • Some AI applications require screening results for potential bias. AIops teams can watch the working results for potential problems. 

All of these questions and strategies apply in some form to the subsets with names like DataOps, MLops, ModelOps, and PlatformOps because they focus on some of the particular parts of the work. 

Also read: From ‘Star Wars’ to streaming wars: How AIops is fueling the intergalactic streaming battle

Is AIops about AI or IT? 

Some companies focus on using AI to Strengthen performance of their servers and databases. They use the term AIops to refer to using AI algorithms to watch for anomalies and, perhaps, predict outages or failures before they happen. The algorithms are good at creating models of expected performance and then creating alerts when the stack starts to perform differently. 

The AI algorithms are particularly useful for noticing security failures. They can, for instance, flag large outflows of data from hackers that stand out because users typically only obtain a small amount of data that fits their need. Unusual data flows are typically indicators of a breach. 

Now that AI routines are becoming more common and integrated to all parts of the stack, some firms are asking how they can support the ongoing work specific to AI tools. That is, juggling the datasets, constructing the models, deploying the models and then rotating them to maintain performance.

How can AIops help security?

While many areas of AIops are focused on practical issues of performance like how quickly a server is responding to a request, some are also using AI algorithms to watch for the kind of anomalies that indicate a leak or unauthorized intrusion. 

A few of the simplest ways that AIops can help with cybersecurity is to watch for large or uncharacteristic outflows of data. If the website is designed to offer small, quick answers with at most one user’s personal information, then a larger block could signal a mistake. 

Some areas that AIops may watch are:

  • Outflows from servers that don’t normally respond or send packets to machines outside the company. 
  • Atypical SQL queries that are new or rarely seen. 
  • Atypical requests for encryption keys.
  • Responses that are encrypted even though they normally aren’t or vice versa. 
  • Unusual load at unusual times. For example, a heavy number of requests in the middle of the night when everyone is normally asleep. 

This approach can be especially useful because security breaches are usually quite rare and difficult for a human to spot. An algorithm can watch thousands of machines and spot the one where the load or the behavior is out of the ordinary. 

AIops algorithms will also adapt with time. The models can be trained and retrained as the workloads shift. This can be useful because some attacks rely upon reactivating older software that is no longer used. For instance, the models can spot that some access mechanisms aren’t in common use and flag them. 

How are the major enterprises handling AIops?

The dominant cloud and service providers all have regular services for exploring and deploying AI. The services began simply, but as users have begun relying upon AI algorithms for production work, the companies have been expanding their services to also offer maintaining datasets and models as necessary. 

The dominant players are also adding special hardware configurations aimed at delivering AI solutions cheaply as possible. Some are building custom hardware that can speed up processing, often dramatically. 

Amazon, for example, developed a custom chip called Inferentia to speed up AI deployments. The chip is optimized for applying a model to the current set of data, a step that is often done many more times than training. The Inferentia is said to be 70% cheaper than using one of AWS’s regular GPU-enabled instances. 

IBM has added AIops to its Cloud Pak for Watson, so the software supports continual delivery of AI-based decisions. The tool helps the team monitoring the AI watch for anomalies and adverse incidents. Intelligent Root Cause Analysis is designed so that the company can understand why decisions are being made, either correctly or incorrectly. 

Google maintains a line of specialized chips for ML that they call TPUs or Tensor Processing Units that can offer faster speeds and lower costs for AIops. They also created a platform called TensorFlow Enterprise to support teams that are using the TensorFlow open-source software in production work. The tool helps teams both explore the power of the algorithms and also deploy work quickly to hardware in Google’s cloud. 

Microsoft has integrated its AI solutions with many of its products. It’s not uncommon to find that the simplest way to work with AI is as a feature for some of its web tools like Dynamics 365, a business management platform. They’re also planning the best solutions for continual delivery of ML solutions with tools like Gandalf, a system that integrates testing with deployment so rollouts of new models and software is safe and curated. 

Nvidia, the major manufacturer of graphics processing units, also supports many cloud options for training and deploying AI models through its CUDA architecture. The company continues to support all clouds that are using Nvidia hardware with a collection of tools like Launchpad.

Also read: AIops lessons learned: Be careful when selecting a vendor

What about AIops startups? 

Many of the companies that specialize in devops and ITops also support AI algorithms as well. The same mechanisms that can detect a failed database or an overloaded server can also detect a problematic server that’s executing an AI routine. Good operations tools can solve many problems that confound AI. 

Companies like NewRelic, DataDog, Splunk, PagerDuty, BigPanda, Turbonomic and DynaTrace are just a few of the leading firms that help track the performance of servers and software. They create event logs from an IT stack and make it available in an easily accessible, often graphical, format. Their dashboards and other tools work well for tracking performance. 

AIops D is a startup designed to roll out microservices that may rely on AI to automate some of its goals. The company, started by Deloitte, also offers consulting services to help create some right microservices to tackle business needs. The goal is to produce a set of largely automated services that handle all of the business processes. 

Companies like Databricks and DataRobot are building clouds that gather data and then apply the best AI algorithms to create models. They began as data warehouses or data lakes and evolved to support sophisticated analysis. 

Is there anything that AIops can’t do? 

AIops platforms tackle a variety of problems but they are only as good as their data. If the data is noisy, inaccurate or full of gaps, the analysis will be less accurate and sometimes completely wrong. Much of the work begins before analysis, when the data is collected.

Analyzing events that are unusual can be a challenge. In some cases, AIops platforms are just tasked with flagging anomalous events. In these cases, strange patterns that don’t match the historical data are easy to identify. 

But in other cases, the AIops platform is expected to create predictions about the future. In these cases, strange or unusual events can produce wrong results. If the AI model is built from the record and it learns how to behave by studying the past, then a new, unusual event will be something it can’t handle because it has no context or history for guidance. 

When the AIops platform helps manage AI models and data gathering, the work of AIops can only support the AI algorithms by making it easier to create new models. It can’t make the algorithms more accurate. AIops can just handle the housekeeping chores. 

Read next: How AIops can benefit businesses

Tue, 02 Aug 2022 13:02:00 -0500 Peter Wayner en-US text/html https://venturebeat.com/applied-ai/what-is-aiops/
Killexams : Can We Trust This Rally?
Arrow sign growth on blue background. Business development to success and growing growth concept. 3d illustration

Markets are moving their way higher. Can we trust the move?

marchmeena29

Brian Dress, CFA - Director of Research, Investment Advisor

No matter whether you are a conservative or an aggressive investor, whether stocks, bonds, or other assets, 2022 has been a year full of struggle and agony.

With such a challenging environment, it is no wonder that investors are excited to see a shift in sentiment, as all the market’s broader indexes finished the last week significantly higher.

On the other hand, we have seen a number of false bottoms in 2022, so many investors are understandably and justifiably suspicious when they see a sharp rally like the one we’ve seen over the past two weeks of trading. In today’s Jarvis® Newsletter, we will try to answer the question on all of our minds: “Can We Trust the Rally?”

In this week’s newsletter, we are going to take a deeper look at the stocks that are leading the market during the last two weeks of positive action and see what insights we can gain from the types of stocks that are outperforming in this mini market resurgence. After all, not all market rallies are predictive of follow-through, which we have learned all too well in the first half of 2022.

One sector that is not participating in the market renaissance of the past two weeks has been commodities. This has been especially painful for us here at Left Brain, since the sectors in which we have had the most conviction in 2022 have been oil/gas and the materials sectors. Over the last month, we have seen down moves on the order of 25% in a number of important commodity indicators, including Energy Select Sector SPDR Fund (XLE), SPDR S&P Metals and Mining ETF (XME), and along with Copper Futures (HG1:COM) on the Chicago Mercantile Exchange.

We will supply you our thoughts on whether this significant pullback is an opportunity for investors to gain exposure to the strongest sectors in first half 2022, or whether we think this is the beginning of just the next bubble to burst. In doing so, we will revisit a couple of our favorite stocks which we have discussed here over the first half of the year.

While it can be easy to get caught up in the daily gyrations in the stock market, we have been reminded in the first half of 2022 that chasing strength has been a recipe for disaster (see financials, tech, healthcare, and consumer staples for clear examples of this in the current year). This summer we are encouraging investors (and ourselves) to think with a long term perspective and identify stocks and bonds that could perform for us over the next 2-5 years. We remain cautious investing with the current environment, but we are using this time to dig deep for securities we think will perform once markets do finally settle.

With that all being said, let’s get into it!

Below is the performance data of key indices, exchange traded fund ("ETFs") for the five trading days between 6/30/22 and 7/7/22:

Table with selected weekly returns data

Yahoo Finance, Left Brain IR

What is/is not Working?

Just 2 of the S&P 500’s 11 sectors were down for the last 5 days of trading, following the pattern from the last month: energy and materials. Again, we will supply our thoughts in the last section of this week’s newsletter whether we think this demonstrates an opportunity for investors. Plenty of positive news on the board this week or as we often say “green on the screen,” which is a welcome change from what we have seen for most of 2022.

The strongest sector ETF we saw this week mirrored the same from the week before and that was SPDR S&P Biotech ETF (XBI), which gained more than 15% over the last week’s trading. This marks the second straight week of double-digit gains for biotech, which is one of the clearest “risk-on” indicators. Other healthcare names have been dragged higher in the wake of biotech, with the SPDR S&P Health Care Services ETF (XHS) and SPDR S&P Pharmaceuticals ETF (XPH) among the strongest ETFs in our list this week. We continue to like healthcare as an investment thesis, with an attractive blend of defensive qualities, along with a decent potential for growth and some dividend yield, especially in the pharmaceutical space.

If you have been following cryptocurrencies even tangentially, you will know that 2022 has been even more brutal in that space than in the wider financial markets. We saw a significant reversal in that move this week in the form of an 11% weekly rise in Grayscale Ethereum Trust (OTCQX:ETHE).

One area of strength we find interesting, given the fact that treasury rates have started to inch back up above 3%, is the strength in municipal bonds. Among our top 20 ETFs this week included Pimco Municipal Income Fund (PMF) and Invesco Value Municipal (IIM). We take this as an indicator that investors are starting to find bond yields attractive after they have risen steadily throughout 2022. We do think there are opportunities developing in quality bonds like munis, but continue to favor bonds further down the credit spectrum in the high yield corporate segment of the bond market.

In terms of the weakest performing ETFs, metals both precious and industrial led the way. In our worst performing ETF list, we saw iShares Silver Trust (SLV), VanEck Gold Miners ETF (GDX), and iPath Series B Bloomberg Copper Subindex Total Return ETN (JJC). Additionally, we saw more weakness in iPath Pure Beta Crude Oil ETN (OIL). Despite recovery in Thursday’s session, commodities lost ground this week, as investors abandoned some of the most profitable plays from the first half of 2022. We will supply our thoughts below whether this is a developing opportunity for investors and reexamine a company in the space that we consider a core holding.

Can We Trust the Risk-On Rally?

One of the top performing ETFs on the board was one we didn’t mention above: ARK Innovation ETF (ARKK), which gained nearly 10% in value over the last 5 days of trading. For those who are unfamiliar with ARKK, the fund holds many of the worst performing shares in 2022, including Roku Inc (ROKU), Teladoc Health (TDOC), Square (SQ), Zoom Video (ZM), and Shopify (SHOP). We continue to think that these stocks, most of which are pre-profit (lose money) and continue to trade at sky-high multiples, are high-risk plays in the current environment.

As we sift through our list of top performing shares of the week, we are struck by the 10, 20, and even 30% moves in just one week’s time, in a number of shares. Among these are again the downtrodden names of 2022, including Beyond Meat (BYND), Wayfair (W), MicroStrategy (MSTR), Etsy (ETSY), and Fastly (FSLY). Note also that many of these shares are among the most heavily shorted on Wall Street.

As our CEO Noland Langford says, short covering is still buying, so we take this as a positive, though we remain skeptical. However, we would caution investors against chasing after these stocks. We are fully aware of the urge many may have to do so, given that many of these stocks are down more than 50% just in 2022. We will gladly stand on the sidelines as some of these pummeled stocks gain momentum, as we generally look to avoid investing in companies with high multiples (price/earnings >30x and/or price/sales ratios in excess of 10x). Since the path of interest rates is most likely to remain higher near term, we think it is difficult for investors to expect sustainable multiple expansion in high multiple companies, particularly those that do not generate positive Free Cash Flow.

We want to remind readers of what we observed in March of this year, in similar stocks. In late March, we saw roughly a 15% advance in the Invesco QQQ Trust (QQQ), which tracks the tech-heavy NASDAQ-100. At the time, this appeared to indicate an end to the selling in high-growth tech and a number of impatient investors decided to reenter tech allocations. The QQQ subsequently dropped more than 25% in the 3 months after the March 29 peak.

Now let’s bring things around to the more positive view: we do think we are moving toward the later innings of the market’s overall selloff. Markets continue to trade erratically and violently, and a week of positive action is just as likely to be followed by more buying as selling. In a market like that, it is easy to get caught up in the short-term gyrations and try to jump in behind strong buying as we all look to catch up on performance for 2022.

Do we believe in the risk-on rally this week? Our answer is a qualified YES. We are becoming more optimistic about the overall market, but still remaining cautious, particularly before earnings season begins next week. At times like these, it is important to think beyond the daily and weekly context and think about the types of companies that are well-positioned from a fundamental standpoint to grow cash flows, profit margins, and sales. These are the businesses that are most likely to deliver outsized returns in the next 3-5 years with defined risk profiles.

It is certainly possible for the Shopifys and Wayfairs of the world to do well in the coming years, but from a risk/reward standpoint, we feel most comfortable looking at lower-multiple profitable companies in the tech space. Examples of these would include Alphabet (GOOG, GOOGL) and International Business Machines (IBM).

What is Going on With Commodities?

For the first half of 2022, the only sectors giving ballast to portfolios were commodity related shares, mainly in oil/gas, but also in metals, mining, materials, and related industries. The Energy Select Sector SPDR Fund (XLE) was as much as 60% higher on a year-to-date basis as recently as early June. However, over the last month, oil prices have fallen significantly, but energy stocks have cratered by an additional factor in the same time period. After more than a 25% drawdown, the XLE now is up just 23% for 2022 and many oil stocks are down for the year!

So what happened? It is hard to pinpoint an exact cause for this move downward. Our view is that much of the selling here has been “flow based.” What does this mean? In search of securities that were performing, many investors added exposure to oil/gas in the 2nd quarter of this year. Perhaps the boat got a bit too loaded to one side and these shares got somewhat ahead of the fundamentals. Late entrants to the space were perhaps unable to take the pain and sold into the downtick over the last few weeks. This is yet another example of why we are cautious to chase performance of stocks of really any sector. We’ve seen this play out too many times in 2022.

We maintain our convictions on oil/gas. Supply/demand conditions remain in the favor of oil producing companies, many of whom have average production costs in the $40-50/barrel range. They are wildly profitable with oil still around $100. We expect the sentiment to shift here dramatically again when these companies report earnings. Many of them are generating unprecedented levels of free cash flow and returning capital to shareholders in the form of dividends and share buybacks. The exact pullback may be an opportunity if you remain underinvested in energy.

Another area that has suffered recently is base metals. One company that we have recommended a number of times in 2022, through our various content channels, is Rio Tinto (RIO). Rio Tinto is one of the world’s largest producers of base metals like copper, aluminum, and iron ore. A number of events have gone against RIO in exact months, including lockdowns in China (the biggest importer of RIO’s products) and continued fears of recession, which would, in theory, decrease demand for industrial metals. RIO shares (blue line) have fallen hard in the last month:

Chart of Rio Tinto (<a href='https://seekingalpha.com/symbol/RIO' title='Rio Tinto Group'>RIO</a>)

TradingView

While many of these fears are justified, we think they may be overplayed. Though base metals prices have been volatile over the years, the company has delivered positive Free Cash Flow in every year since 2012, with strong growth on that line each of the last three years. The shares currently trade at a price to earnings multiple below 5x and at current prices, the dividend yield stands near 14%! Clearly investors are forecasting that RIO will be able to maintain earnings, but our view is that if the recession is less severe than advertised, this stock has plenty of room to run to the upside. As a bonus, consider Cleveland-Cliffs (CLF), an integrated steel producer whose stock has also been pounded in exact weeks, has very little debt, and also trades at a very low multiple.

Takeaways from this Week

It was good to see renewed buying in the markets this week. We are becoming more optimistic, but remaining cautious, as some of this week’s leaders were the most heavily shorted and downtrodden names of 2022. We think investors would do well to zoom out and keep focus on long-term investing, as many high-quality stocks are quite cheap in the context of the current market environment.

We have been frustrated with the weakness in commodities, but our view is that a lot of the selling has been “flow-based” and headline-oriented. We think the fundamentals remain strong in commodities, but especially in oil/gas. We think investors should consider whether the 25% drop in the month of June is an opportunity here, especially in cash-generative stocks that trade at extremely low multiples like Rio Tinto.

Fri, 08 Jul 2022 13:44:00 -0500 en text/html https://seekingalpha.com/article/4522478-can-we-trust-this-rally
Killexams : (CAGR) of 20.1% , Predictive Analytics In Banking Market Set for Explosive Growth | Alteryx Inc., Tableau Software Inc., KXEN

Predictive Analytics In Banking Market Size, Share & Trends Analysis – Global Opportunity Analysis And Industry Forecast 2030  , Covid 19 Outbreak Impact research report added by Report Ocean, is an in-depth analysis of market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market’s historic and forecast market growth by geography. It places the market within the context of the wider Predictive Analytics In Banking market, and compares it with other markets., market definition, regional market opportunity, sales and revenue by region, manufacturing cost analysis, Industrial Chain, market effect factors analysis, Predictive Analytics In Banking market size forecast, market data & Graphs and Statistics, Tables, Bar &Pie Charts, and many more for business intelligence. Get complete Report (Including Full TOC, 100+ Tables & Figures, and Chart). – In-depth Analysis Pre & Post COVID-19 Market Outbreak Impact Analysis & Situation by Region

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Key Segments Studied in the Global Predictive Analytics In Banking Market

The global predictive analytics in banking market size was US$ 1.9 billion in 2021. The global predictive analytics in banking market is forecast to grow to US$ 9.91 billion by 2030 by registering a compound annual growth rate (CAGR) of 20.1% during the forecast period from 2022 to 2030.

Predictive analytics is a modern technology used in the banking industry to gain client insights. The technology is widely used to offer a more individualized and better client experience. It assists financial businesses with risk assessment, regulatory management, and customer relationship management (CRM). Predictive analytics could also be used by credit card firms to create credit lines for their clients. Apart from that, insurance firms can utilize predictive analytics to determine premium levels.

Factors Influencing the Market

There are millions of IoT devices in use worldwide, and fraud activities, including money laundering, financial fraud, and card fraud are rising notably. Additionally, property payments are anticipated in and out of business, and customers are fostering the expansion of global banking predictive analytics.

Other factors such as rising demand from developed economies and the growing usage of artificial intelligence (AI) in mobile banking applications will also contribute to the growth of predictive analytics in banking market during the forecast period.

Rising awareness among consumers related to predictive analytics in banking will also benefit the market. On the flip side, issues related to the implementation of predictive analytics in banking may limit the growth of the market during the analysis period.

COVID-19 Impact Analysis

The COVID-19 pandemic has significantly raised the need to adopt crucial practices to avoid fraud. The cases of fraud increased during the pandemic, which forced institutions to take mandatory steps. In addition, the use of IoT devices surged during the pandemic as digitalization was triggered. Thus, it was opportunistic for predictive analytics in the banking market.

Regional Analysis

North America is forecast to dominate the predictive analytics in banking market, owing to the rising adoption of advanced technology in the region. In addition, strict regulations imposed by the government bodies will significantly raise the demand for predictive analytics in banking to boost data security. The Asia-Pacific predictive analytics in banking market will also record significant growth due to the rising awareness of the benefits of predictive analytics in banking. Apart from that, the growing number of banking institutions in the region will escalate growth prospects for this regional market during the forecast period.

Competitors in the Market

  • Alteryx Inc.
  • Tableau Software Inc.
  • KXEN
  • SAS Institute Inc.
  • TIBCO Software Inc
  • Microsoft corporation
  • SAP SE
  • Salford Systems
  • IBM Corporation
  • Oracle Corporation
  • FICO
  • Teradata Corporation
  • Other Prominent Players

Market Segmentation

The global predictive analytics in banking market segmentation focuses on Component, Deployment Model, Organization Size, Application, and Region.

By Component

By Deployment Model

By Organization Size

  • Large Enterprises
  • SME’s

By Application

  • Fraud Detection & Prevention
  • Customer Management
  • Sales & Marketing
  • Workforce Management
  • Others

 

Our market research provides vital intelligence on market size, business trends, industry structure, market share, and market forecasts that are essential to developing business plans and strategy.

A combination of factors, including COVID-19 containment situation, end-use market recovery & Recovery Timeline of 2020/ 2021

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The impact of COVID-19 on the future development of the industry is pointed out.

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  • Market Behavior/ Level of Risk and Opportunity
  • End Industry Behavior/ Opportunity Assessment
  • Expected Industry Recovery Timeline

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Geographical Breakdown: The regional and country breakdowns section gives an analysis of the market in each geography and the size of the market by geography and compares their historic and forecast growth. It covers the impact and recovery path of Covid 19 for all regions, key developed countries and major emerging markets.

Countries: Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czech Republic, Denmark, Egypt, Finland, France, Germany, Hong Kong, India, Indonesia, Ireland, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, New Zealand, Nigeria, Norway, Peru, Philippines, Poland, Portugal, Romania, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, Thailand, Turkey, UAE, UK, USA, Venezuela, Vietnam

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Analytical Market Highlights & Approach
The report provides the rigorously studied and evaluated data of the top industry players and their scope in the market by means of several analytical tools. The analytical tools such as Porters five forces analysis, feasibility study, SWOT analysis, and ROI analysis have been practiced reviewing the growth of the key players operating in the market.

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Key Points Covered in Predictive Analytics In Banking Market Report:

Global Predictive Analytics In Banking Market Research Report
Section 1: Global Predictive Analytics In Banking  Industry Overview
Section 2: Global Economic Impact on Predictive Analytics In Banking  Industry
Section 3: Global Market Competition by Industry Producers
Section 4: Global Productions, Revenue (Value), according to Regions
Section 5: Global Supplies (Production), Consumption, Export, Import, geographically
Section 6: Global Productions, Revenue (Value), Price Trend, Product Type
Section 7: Global Market Analysis, on the basis of Application
Section 8: Predictive Analytics In Banking  Market Pricing Analysis
Section 9: Market Chain, Sourcing Strategy, and Downstream Buyers
Section 10: Strategies and key policies by Distributors/Suppliers/Traders
Section 11: Key Marketing Strategy Analysis, by Market Vendors
Section 12: Market Effect Factors Analysis
Section 13: Global Predictive Analytics In Banking  Market Forecast

……..and view more in complete table of Contents

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Killexams : Global Digital Twin Market Set To Reach USD 113.3 Billion By 2030, Thriving With A CAGR Of 42.7% | Growth Market Reports

PUNE, India, Aug. 3, 2022 /PRNewswire/ -- According to a exact market study published by Growth Market Reports, titled, "Global Digital Twin Market" by Types (Products, Systems, Processes, and Others), Technologies (IoT & IIoT, Blockchain, Artificial Intelligence & Machine Learning, Big Data Analytics, and Others), Enterprise (Large Enterprises and SMEs), Applications (Product Design & Development, Performance Monitoring, Predictive Maintenance, Inventory Management, Business Optimization, and Others), Industry Verticals (Manufacturing, Automotive, Aerospace & Defense, Energy & Utilities, Oil & Gas, Healthcare, and Others), and Regions: Size, Share, Trends and Opportunity Analysis, 2021-2030", the market is projected to reach USD 113.3 billion in 2030 and is expected to expand at a CAGR of 42.7% during the forecast period. The global digital twin market is projected to expand at a rapid pace, due to the emerging economies, growing population, increasing per capita income, and the rising adoption of digitalization among consumers.

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Key Market Players Profiled in the Report

The report covers comprehensive data on emerging trends, market drivers, growth opportunities, and restraints that can change the market dynamics of the industry. It provides an in-depth analysis of the market segments which include types, technologies, enterprises, applications, industry verticals, and competitor analysis.

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This report also includes a complete analysis of industry players that covers their latest developments, product portfolio, pricing, mergers, acquisitions, and collaborations. Moreover, it provides crucial strategies that are helping them to expand their market share.

Highlights on the segments of the Digital Twin Market

In terms of types, the global digital twin market is segmented into products, systems, processes, and others. The systems segment is anticipated to expand at a sustainable CAGR during the forecast period, owing to the increasing use of digital twins in various applications by consumers.

Based on technologies, the global digital twin market is segmented into IoT & IIoT, blockchain, artificial intelligence & machine learning, big data analytics, and others. The artificial intelligence & machine learning segment is expected to grow at a significant pace during the forecast period, as digital twin technology is easy to retrain, reuse, and adapt to the existing environment. The AI-enabled digital twin technology offers repeated use and improving functionality by enhancing productivity.

On the basis of enterprises, the global digital twin market is segregated into large enterprises and SMEs. The large enterprise segment is expected to expand at a rapid rate during the forecast period, due to the growing adoption of digital twins by large enterprises for predicting success, maintenance requirements, and failure in enterprises operations.

Based on regions, the market is segmented into five major regions, namely, North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. The market in the Asia Pacific is projected to expand at a significant CAGR during the forecast period, owing to the growth in large-scale industrialization and rising population in the region. The market in North America is anticipated to hold a substantial share of the global digital twin market, due to the increasing deployment of automation solutions by the manufacturing industries.

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Key Takeaways from the Study:

  • The digital twin market in North America is expanding and dominating as compared to other regional markets, as the deployment of digital twin aids in improving production lines and downstream operations.

  • The key players in the North America region investing significantly in the digital twin market. North America becomes a major hub for technological innovations. For instance, in August 2021, the General Electric company upgrades its on-premise analytics software, which is Proficy CSense.

  • The market in the Asia Pacific region is attributed to the proliferation of connected devices. Countries including India, China, Australia, and South Korea have a great potential for integrating digital transformation.

  • Automotive segment is anticipated to grow at a significant pace, owing to the rising deployment of automation solutions by the manufacturing industries. The rise in the usage of digital twins for manufacturing, simulation, designing, MRO (maintenance, repair, and overhaul), and after-sales is correlated with the expansion of the automobile market.

  • Key players in the market introducing innovative and advanced products. Companies are encouraged to invest in the R&D of goods and automated processes by the fierce rivalry among large firms.

Read 213 Pages Research Report with Detailed ToC on "Global Digital Twin Market by Types (Products, Systems, Processes, and Others), Technologies (IoT & IIoT, Blockchain, Artificial Intelligence & Machine Learning, Big Data Analytics, and Others), Enterprise (Large Enterprises and SMEs), Applications (Product Design & Development, Performance Monitoring, Predictive Maintenance, Inventory Management, Business Optimization, and Others), Industry Verticals (Manufacturing, Automotive, Aerospace & Defense, Energy & Utilities, Oil & Gas, Healthcare, and Others), and Regions (North America, Latin America, Europe, Asia Pacific, and Middle East & Africa) - Global Industry Analysis, Growth, Share, Size, Trends, and Forecast 2021 – 2030"

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Key Segments Covered

Types

  • Products

  • Systems

  • Processes

  • Others

Technologies

Enterprise

Applications

Industry Verticals

  • Manufacturing

  • Automotive

  • Aerospace & Defense

  • Energy & Utilities

  • Oil & Gas

  • Healthcare

  • Others

By Regions

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About Growth Market Reports:

Growth Market Reports provides global enterprises as well as medium and small businesses with unmatched quality "Market Research Reports" and "Industry Intelligence Solutions". Growth Market Reports has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domains.

Our key analysis segments, though not restricted to the same, include market entry strategies, market size estimations, market trend analysis, market opportunity analysis, market threat analysis, market growth/fall forecasting, primary interviews, and secondary research & consumer surveys.

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Wed, 03 Aug 2022 12:29:00 -0500 en-US text/html https://finance.yahoo.com/news/global-digital-twin-market-set-123000484.html
Killexams : Machine Learning as a Service Market Estimated to Cross USD 302.66 Billion at a CAGR of 36.2% by 2030 - Report by Market Research Future (MRFR)

Market Research Future

Increasing Adoption of Cloud Computing to Boost MLaaS Market Growth

New York, US, July 05, 2022 (GLOBE NEWSWIRE) -- According to a comprehensive research report by Market Research Future (MRFR), “Machine Learning as a Service Market Analysis by Component (Software tools, Cloud APIs, Web-based APIs), By Application (Network analytics, Predictive maintenance, Augmented reality), By Deployment, By End-User (Manufacturing, Healthcare, BFSI, Transportation, Government, Retail)- Forecast 2030” valuation is poised to reach USD 302.66 Billion by 2030, registering an 36.2% CAGR throughout the forecast period (2021–2030).

MLaaS Market Overview

Technological developments coupled with the increase in innovation and research activities across the globe will offer robust opportunities for the Mlaas market over the forecast period.

Machine Learning as a Service Market Report Scope:

Report Metric

Details

Market Size

USD 302.66 Billion by 2030

CAGR

36.2% From 2021 To 2030

Base Year

2020

Forecast Period

2021 To 2030

Historical Data

2019

Forecast Units

Value (USD Billion)

Report Coverage

Revenue Forecast, Competitive Landscape, Growth Factors, and Trends

Segments Covered

Component, Application and Region

Geographies Covered

North America, Europe, Asia-Pacific, and Rest of the World (RoW)

Key Vendors

Yottamine Analytics (U.S.), Fuzzy.ai (Canada), AT&T (U.S.), Amazon Web Services (U.S.), IBM (U.S.), Microsoft (U.S.), BigML (U.S.), Google (U.S.), Ersatz Labs, Inc. (U.S.), and Sift Science, Inc. (U.S.)

Key Market Opportunities

Technological Developments to offer Robust Opportunities

Key Market Drivers

Increasing Adoption of Cloud Computing to Boost Machine Learning as a Service Market Growth

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Drivers 
Increasing Adoption of Cloud Computing to Boost Market Growth 
The increasing adoption of cloud computing technology coupled with the use of social media platforms will boost market growth over the forecast period. Cloud computing is now widely used by all companies that supply enterprise storage solutions. Data analysis is performed online using cloud storage, giving the advantage of evaluating real-time data collected on the cloud. Cloud computing enables the analysis of data from any location and any time. Furthermore, using the cloud to execute machine learning allows businesses to get useful data, such as consumer behaviour and purchasing trends, virtually from linked data warehouses, lowering infrastructure and storage costs. As a result, the sector for machine learning as a service sector is growing as cloud computing technology becomes more widely adopted.

Compliance Issues to act as Market Restraint 
Compliance and government issues and dearth in the availability of skilled consultants for deploying machine learning services may act as market restraints over the forecast period.

Lack of Knowledge to Remain Market Challenge 
The lack of knowledge and stringent compliance difficulties may act as market challenges over the forecast period.

Machine Learning as a Service Market Segments

The machine learning as a service market is bifurcated based on component, application, deployment, end user, and organization size.

By component, the machine learning as a service market is segmented into web-based APIs, cloud APIs, and software tools.

By application, network analytics will lead the market over the forecast period.

By deployment, the machine learning as a service market is segmented into on premise and cloud.

By end user, retail will dominate the market over the forecast period.

By organization size, the SME will spearhead the market over the forecast period.

Browse In-depth Market Research Report (100 Pages) on Machine Learning as a Service Market:

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Machine Learning as a Service Market Regional Analysis 

North America to Head MLaaS Market 
Because of increased growth in nations like Canada and the United States, the North American area holds the highest proportion of the Mlaas business. These countries are home to a diverse range of small and large start-ups. As a result, the market for machine learning as a service in North America is growing. North America is the fastest-developing region in the global machine learning as a service business in terms of technology advances and utilization. It has the infrastructure and financial resources to invest in machine learning as a service. In addition, higher defence spending and technological advancements in the telecommunications industry are expected to drive market growth throughout the forecast period.

Government data security requirements are predicted to be a major driver of the machine learning services market. The market is predicted to be fueled by services like security information & cloud applications. Furthermore, the presence of industry titans such as IBM, Google, Microsoft, & Amazon Web Services, as well as a diverse product offering, has boosted demand for machine learning in this region. Furthermore, the market players are likely to benefit from the expansion of artificial intelligence and cognitive computing by leveraging various industry applications like natural language processing, predictive analytics, fraud detection and management, and computer vision.

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Because of the robust innovation ecosystem, which is fueled by strategic federal investments in advanced technology and complemented via the presence of entrepreneurs and visionary scientists coming together from renowned research institutions, North America is expected to grasp a significant share of the market. A huge expansion of 5G, internet of things, and linked devices is also occurring in the region. Because each of the major technology companies has a sizable public cloud infrastructure and machine learning platforms, machine learning-as-a-service is now a reality for those looking to use AI for everything from customer service to the robotic process automation, analytics, marketing, and predictive maintenance, among other things.

APAC to Have Admirable Growth in Machine Learning as a Service Market 

With the greatest CAGR, Asia-Pacific is predicted to be the quickest developing regional segment over the forecast period. Leading companies are concentrating their efforts in Asia-Pacific to expand their operations, as the region is likely to see rapid development in the deployment of security services, particularly in the BFSI sector. To provide better customer service, industry participants are realizing the significance of providing multi-modal platforms. The rise in AI application adoption is likely to be the primary trend driving market growth in the area. Furthermore, government organizations have taken important steps to accelerate the adoption of machine learning & related technologies in this region. During the forecast period, Asia-Pacific is expected to have the highest CAGR. This is due to an increase in the use of security services, particularly in the BFSI sector, as well as increased awareness and long-term expansion of the IT sector in the region.

COVID-19 Analysis 
Artificial intelligence is likely to aid in the fight against the COVID-19 pandemic. COVID-19 cases are being tracked and traced in several countries utilizing population monitoring approaches. Researchers in South Korea, for example, track coronavirus cases using surveillance camera footage & geo-location data. Data scientists use machine intelligence algorithms for anticipating the location of the next outbreak and notify the appropriate authorities, allowing for real-time illness tracking. During the projection period, such active endeavours are projected to increase need for machine intelligence solutions.

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Machine Learning as a Service Market Competitive Analysis

Dominant Key Players on Machine Learning as a Service Market Covered are:

  • Apple Inc. (US)

  • IBM Corporation (US)

  • BAE Systems (UK)

  • LG Electronics (South Korea)

  • Google Inc. (US)

  • Microsoft Corporation (US)

  • Digital Reasoning Systems Inc. (US)

  • ABB Limited (Switzerland)

  • General Electric Co. (US)

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Emotion Analytics Market Research Report: By Type, Technologies, Solution, End-Users - Forecast Till 2030

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Gesture Recognition Market Research Report: Information By Technology, Application, Product - Forecast till 2030

About Market Research Future:

Market Research Future (MRFR) is a global market research company that takes pride in its services, offering a complete and accurate analysis regarding diverse markets and consumers worldwide. Market Research Future has the distinguished objective of providing the optimal quality research and granular research to clients. Our market research studies by products, services, technologies, applications, end users, and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help answer your most important questions.

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