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Killexams : IBM Analytics education - BingNews https://killexams.com/pass4sure/exam-detail/000-583 Search results Killexams : IBM Analytics education - BingNews https://killexams.com/pass4sure/exam-detail/000-583 https://killexams.com/exam_list/IBM Killexams : Edology partners with IBM to launch Post Graduate Certificate Program in Data Science

Gurugram (Haryana) [India], July 30 (ANI/NewsVoir): Edology has announced a partnership with IBM, one of the world's top leading and reputed corporations, to introduce its Post Graduate Certificate Program in Data Science for working professionals and everyone wanting to enter the field of Data Science. Developed by IBM inventors and experts who hold numerous patents in the field of Data Science, this is the first IBM programme that has been completely designed by IBM and is being delivered by its faculty.

"The programme for the Edology x IBM Data Science course is a very special offering from IBM, and this is one-of-a-kind initiative," according to Hari Ramasubramanian, Leader, Business Development and Academia Relationships, IBM Expert Labs, India/South Asia. He further added, "There is a strong demand for skilled technology and trained professionals across the industry. Data science is not confined to IT. It includes all the verticals one can imagine-from board meetings to sports, data science brings a lot of value to organizations worldwide. For students, as well as professionals with experience, if you want to fast track your career on to the next level, this is the course you should be doing."

"The IBM Data Science certificate program through the Edology platform, will equip to adapt to the dynamics in the industry and drive technology innovation," said, Vithal Madyalkar, Program Director, IBM Innovation Centre for Education, India/South Asia. "The Data Science course modules will provide deep practical knowledge, coupled with broad-based industry alignment, interaction, talent discoverability as well as excellence in their professional practice."

A global Ed-Tech company, Edology helps students and professionals all around the world advance their careers in a variety of subjects, including data science, artificial intelligence, machine learning, cyber security, and more.

Unique Offerings of the IBM x Edology PG Certificate Programme in Data Science:

- 100+ hours of Live classes by IBM experts

- Globally recognized IBM digital badge

- Job opportunities with 300+ corporate partners

- Edology-IBM Award for Top Performers

- 1 on 1 mentorship from industry experts

- 1 day networking session with IBM team

- Guaranteed interview with IBM for top performers in each cohort

- Dedicated career assistance team

Sumanth Palepu, the Business Head at Edology, states, "Statistical estimates reveal that the worldwide market size for Data Science and analytics is anticipated to reach around a whopping $450 billion by 2025, which also means that the rivalry would be quite severe at the employee level, the competition will be very fierce. Thus, this collaboration with IBM is now more essential than ever, so that we are collectively able to deliver advanced level teaching to the students and working professionals and they get first-hand industry knowledge with our IBM experts."

www.youtube.com/watch?v=rjWGU_k2Dhg

Edology is a Global Ed-Tech Brand that provides industry-powered education and skills to students and professionals across the world, to help them achieve fast-track career growth. Launched in 2017, Edology connects professionals from across the globe with higher education programmes in the fields of law, finance, accounting, business, computing, marketing, fashion, criminology, psychology, and more.

It's a part of Global University Systems (GUS), an international network of higher-education institutions, brought together by a shared passion of providing industry-driven global education accessible and affordable. All the programs of Edology are built with the objective of providing its learners career enhancement and strong CV credentials, along with a quality learning experience.

The courses offered by Edology include Data Science, Certification in AI and Machine Learning, Data Analytics, PGP in International Business, PGP in Renewable Energy Management, PGP in Oil and Gas Management among others. These offerings are done through hands-on industry projects, interactive live classes, global peer-to-peer learning and other facilities.

This story is provided by NewsVoir. ANI will not be responsible in any way for the content of this article. (ANI/NewsVoir)

Fri, 29 Jul 2022 21:31:00 -0500 en text/html https://www.bignewsnetwork.com/news/272637512/edology-partners-with-ibm-to-launch-post-graduate-certificate-program-in-data-science
Killexams : IBM Research Rolls Out A Comprehensive AI And Platform-Based Edge Research Strategy Anchored By Enterprise Use Cases And Partnerships

I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.

Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.

Edge In, not Cloud Out

In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.

A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.

“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.

IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.

IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).

IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.

It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.

Why edge is important

Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.

Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.

Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.

IBM at the Edge

In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.

Example #1 – McDonald’s drive-thru

Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.

McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.

Example #2 – Boston Dynamics and Spot the agile mobile robot

According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Excellerate future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.

To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.

IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.

IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.

IBM market opportunities

Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.

Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.

Challenges with scaling

“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”

Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.

Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.

IBM AI entry points at the edge

IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.

IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.

Industry 4.0

There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.

Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Excellerate quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:

  • Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
  • Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
  • Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
  • Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.

Maximo Application Suite

IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.

IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.

Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.

Day-2 AI Operations (retraining and scaling)

Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.

IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.

A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).

“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”

Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.

Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.

The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.

Data Fabric Extensions to Hub and Spokes

IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.

  1. First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
  2. Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Excellerate the model. Identification is also made for atypical data that is judged worthy of human attention.
  3. The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
  4. Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.

In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.

Multicloud and Edge platform

In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.

For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.

Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.

Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.

First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).

Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.

Telco network intelligence and slice management with AL/ML

Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:

  • Reduced operating costs
  • Improved efficiency
  • Increased distribution and density
  • Lower latency

The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.

An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.

5G network slicing and slice management

Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.

5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.

Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.

Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”

In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:

  • End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
  • Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
  • Improved operational efficiency and reduced cost

Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.

5G radio access

Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.

O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.

The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:

  • Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
  • Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
  • Utilization of the antenna control plane to optimize throughput
  • Primitives for forecasting, anomaly detection and root cause analysis using ML
  • Opportunity of value-added functions for O-RAN

IBM Cloud and Infrastructure

The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.

IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.

As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).

Wrap up

Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.

IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.

IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.

Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.

Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.

However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.

It is reassuring that IBM has a plan and that its plan is sound.

Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Infinidat, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, MulteFire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX, and Movandi.

Mon, 08 Aug 2022 03:51:00 -0500 Paul Smith-Goodson en text/html https://www.forbes.com/sites/moorinsights/2022/08/08/ibm-research-rolls-out-a-comprehensive-ai-and-ml-edge-research-strategy-anchored-by-enterprise-partnerships-and-use-cases/
Killexams : Education and Learning Analytics Market Research:

San Francisco, United States: The Education and Learning Analytics research report covers global current market size estimation, market situation, structure, products, leading industry players, segmentation by types, and applications. The Education and Learning Analytics market study focuses on the characteristics that have a significant influence on the Education and Learning Analytics market and might have a massive effect on its future growth. Also included in the research are details on the drivers that lead to growth as well as the market’s limitations and recent gains.The education and learning analytics market is expected to register a CAGR 16.9% during the forecast period 2019–2026. Education and Learning Analytics Market Report studies explore the effects of COVID-19 on the upstream, midstream, and downstream sectors of the industry. In addition, this analysis provides extensive market estimations by putting an emphasis on data covering numerous factors that encompass market dynamics such as market drivers, market barriers, market opportunities, market risks, and industry news and trends.

Competitive Landscape

Some of the prominent players operating in the Education and Learning Analytics market are Blackboard Inc. (U.S.), Microsoft (U.S.), IBM Corporation (U.S.), Oracle Corporation (U.S.), Pearson Inc. (U.K), Saba Software Inc. (U.S.), McGraw-Hill Education (U.S.), SAP AG (Germany), and D2L Corporation (Canada), Cornerstone OnDemand (U.S.), Jenzabar (U.S.), Knewton (U.S.), and Kronos (U.S.)

Get Free Request sample Report @ https://straitsresearch.com/report/education-and-learning-analytics-market/request-sample

Many industries might benefit from the substantial market research that the Education and Learning Analytics market report does. Every business owner wants to know how much demand there is for new products, and this study is an excellent resource. As an added bonus, the most recent market changes are always taken into account. You may keep an eye on your competitors and their strategies for development by reading the Education and Learning Analytics market research reports. It also conducts extensive study for the years 2022-2030 in order to deliver business owners with opportunities in the future.

This research also provides a dashboard view of prominent Organization, highlighting their effective marketing tactics, market share and most recent advances in both historical and current settings.

Global Education and Learning Analytics Market: Segmentation

As a result of the Education and Learning Analytics market segmentation, the market is divided into sub-segments based on product type, application, as well as regional and country-level forecasts.

By Tools,Predictive Analytics, Content Analytics, Adaptive Learning Analytics, Others,
By Deployment, On-Premise, On Cloud,
By Service, Managed Services, Professional Services,
By End-User, Academic, Enterprise/Corporate,

The report forecasts revenue growth at all the geographic levels and provides an in-depth analysis of the latest industry trends and development patterns from 2022 to 2030 in each of the segments and sub-segments. Some of the major geographies included in the market are given below:

  • North America (U.S., Canada)
  • Europe (U.K., Germany, France, Italy)
  • Asia Pacific (China, India, Japan, Singapore, Malaysia)
  • Latin America (Brazil, Mexico)
  • Middle East & Africa

This Report is available for purchase on Buy Education and Learning Analytics Market Report

Key Highlights

  • In order to explain Education and Learning Analytics the following: introduction, product type and application, market overview, market analysis by countries, market opportunities, market risk, and market driving forces
  • The purpose of this study is to examine the manufacturers of Education and Learning Analytics, including profile, primary business, and news, sales and price, revenue, and market share.
  • To provide an overview of the competitive landscape among the leading manufacturers in the world, including sales, revenue, and market share of Education and Learning Analytics percent
  • In order to illustrate the market subdivided by kind and application, complete with sales, price, revenue, market share, and growth rate broken down by type and application
  • To conduct an analysis of the main regions by manufacturers, categories, and applications, covering regions such as North America, Europe, Asia Pacific, the Middle East, and South America, with sales, revenue, and market share segmented by manufacturers, types, and applications.
  • To conduct an investigation into the production costs, essential raw materials, and production method, etc.

Principal Motives Behind the Purchase:

  • To get deep analyses of the industry and to have a complete comprehension of the commercial landscape of the global market.
  • Analyse the production processes, key problems, and potential solutions in order to reduce the potential for future problems.
  • The goal of this study is to get an understanding of the most influential driving and restraining factors in the Education and Learning Analytics industry as well as the influence that this market has on the worldwide market.
  • Gain an understanding of the market strategies that are now being used by the most successful firms in their respective fields.
  • In order to have an understanding of the market’s future and potential.

Read Full Report with Table of Content and Figures Education and Learning Analytics Market Report with TOC

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Killexams : Predictive Analytics Market Worth $38 Billion by 2028

NEW YORK, Aug. 9, 2022 /PRNewswire/ -- The Insight Partners published latest research study on "Predictive Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component [Solution (Risk Analytics, Marketing Analytics, Sales Analytics, Customer Analytics, and Others) and Service], Deployment Mode (On-Premise and Cloud-Based), Organization Size [Small and Medium Enterprises (SMEs) and Large Enterprises], and Industry Vertical (IT & Telecom, BFSI, Energy & Utilities, Government and Defence, Retail and e-Commerce, Manufacturing, and Others)", the global predictive analytics market size is projected to grow from $12.49 billion in 2022 to $38.03 billion by 2028; it is expected to grow at a CAGR of 20.4% from 2022 to 2028.

Download PDF Brochure of Predictive Analytics Market Size - COVID-19 Impact and Global Analysis with Strategic Developments at: https://www.theinsightpartners.com/sample/TIPTE100000160/

Predictive Analytics Market Report Scope & Strategic Insights:

Report Coverage

Details

Market Size Value in

US$ 12.49 Billion in 2022

Market Size Value by

US$ 38.03 Billion by 2028

Growth rate

CAGR of 20.4% from 2022 to 2028

Forecast Period

2022-2028

Base Year

2022

No. of Pages

229

No. Tables

142

No. of Charts & Figures

100

Historical data available

Yes

Segments covered

Component, Deployment Mode, Organization Size, and Industry Vertical

Regional scope

North America; Europe; Asia Pacific; Latin America; MEA

Country scope

US, UK, Canada, Germany, France, Italy, Australia, Russia, China, Japan, South Korea, Saudi Arabia, Brazil, Argentina

Report coverage

Revenue forecast, company ranking, competitive landscape, growth factors, and trends


Predictive Analytics Market: Competitive Landscape and Key Developments

IBM Corporation; Microsoft Corporation; Oracle Corporation; SAP SE; Google LLC; SAS Institute Inc.; Salesforce.com, inc.; Amazon Web Services; Hewlett Packard Enterprise Development LP (HPE); and NTT DATA Corporation are among the leading players profiled in this report of the predictive analytics market. Several other essential predictive analytics market players were analyzed for a holistic view of the predictive analytics market and its ecosystem. The report provides detailed predictive analytics market insights, which help the key players strategize their growth.

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In 2022, Microsoft partnered with Teradata, a provider of a multi-cloud platform for enterprise analytics, for the integration of Teradata's Vantage data platform into Microsoft Azure.

In 2021, IBM and Black & Veatch collaborated to assist customers in keeping their assets and equipment working at peak performance and reliability by integrating AI with real-time data analytics.

In 2020, Microsoft partnered with SAS for the extension of their business solutions. As a part of this move, the companies will migrate SAS analytical products and solutions to Microsoft Azure as a preferred cloud provider for SAS cloud.

Increase in Uptake of Predictive Analytics Tools Propels Predictive Analytics Market Growth:

Predictive analytics tools use data to state the probabilities of the possible outcomes in the future. Knowing these probabilities can help users plan many aspects of their business. Predictive analytics is part of a larger set of data analytics; other aspects of data analytics include descriptive analytics, which helps users understand what their data represent; diagnostic analytics, which helps identify the causes of past events; and prescriptive analytics, which provides users with practical advice to make better decisions.

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Prescriptive analytics is similar to predictive analytics. Predictive modeling is the most technical aspect of predictive analytics. Data analysts perform modeling with statistics and other historical data. The model then estimates the likelihood of different outcomes. In e-commerce, predictive modeling tools help analyze customer data. It can predict how many people are likely to buy a certain product. It can also predict the return on investment (ROI) of targeted marketing campaigns. Some software-as-a-service (SaaS) may collect data directly from online stores, such as Amazon Marketplace.

Predictive analytics tools may benefit social media marketing by guiding users to plan the type of content to post; these tools also recommend the best time and day to post. Manufacturing industries need predictive analytics to manage inventory, supply chains, and staff hiring processes. Transport planning and execution are performed more efficiently with predictive analytics tools. For instance, SAP is a leading multinational software company. Its Predictive Analytics was one of the leading data analytics platforms across the world. Now, the software is gradually being integrated into SAP's larger Cloud Analytics platform, which does more business intelligence (BI) than SAP Predictive Analytics. SAP Analytics Cloud, which works on all devices, utilizes artificial intelligence (AI) to Excellerate business planning and forecasting. This analytics platform can be easily extended to businesses of all sizes.

North America is one of the most vital regions for the uptake and growth of new technologies due to favorable government policies that boost innovation, the presence of a substantial industrial base, and high purchasing power, especially in developed countries such as the US and Canada. The industrial sector in the US is a prominent market for security analytics. The country consists of a large number of predictive analytics platform developers. The COVID-19 pandemic enforced companies to adopt the work-from-home culture, increasing the demand for big data and data analytics.

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The pandemic created an enormous challenge for businesses in North America to continue operating despite massive shutdowns of offices and other facilities. Furthermore, the surge in digital traffic presented an opportunity for numerous online frauds, phishing attacks, denial of inventory, and ransomware attacks. Due to the increased risk of cybercrimes, enterprises began adopting advanced predictive analytics-based solutions to detect and manage any abnormal behavior in their networks. Thus, with the growing number of remote working facilities, the need for predictive analytics solutions also increased in North America during the COVID-19 pandemic.

Predictive Analytics Market: Industry Overview

The predictive analytics market is segmented on the basis of component, deployment mode, organization size, industry vertical, and geography. The predictive analytics market analysis, by component, is segmented into solutions and services. The predictive analytics market based on solution is segmented into risk analytics, marketing analytics, sales analytics, customer analytics, and others. The predictive analytics market analysis, by deployment mode, is bifurcated into cloud and on-premises. The predictive analytics market, by organization size, is segmented into large enterprises, and small and medium-sized enterprises (SMEs). The predictive analytics market, by vertical, is segmented into BFSI, manufacturing, retail and e-Commerce, IT and telecom, energy and utilities, government and defense, and others.

In terms of geography, the predictive analytics market is categorized into five regions—North America, Europe, Asia Pacific (APAC), the Middle East & Africa (MEA), and South America (SAM). The predictive analytics market in North America is sub segmented into the US, Canada, and Mexico. Predictive analytics software is increasingly being adopted in multiple organizations, and cloud-based predictive analytics software solutions are gaining significance in SMEs in North America. The highly competitive retail sector in this region is harnessing the potential of this technique to efficiently transform store layouts and enhance the customer experience in various businesses. In a few North American countries, retailers use smart carts with locator beacons, pin-sized cameras installed near shelves, or the store's Wi-Fi network to determine the footfall in the store, provide directions to a specific product section, and check key areas visited by customers. This process can also provide basic demographic data for parameters such as gender and age.

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Wal-Mart, Costco, Kroger, The Home Depot, and Target have their origin in North America. The amount of data generated by stores surges with the rise in sales. Without implementing analytics solutions, it becomes difficult to manage such vast data that include records, behaviors, etc., of all customers. Players such as Euclid Analytics offer spatial analytics platforms for retailers operating offline to help them track customer traffic, loyalty, and other indicators associated with customer visits. Euclid's solutions include preconfigured sensors connected to switches that are linked through a network. These sensors can detect customer calls from devices that have Wi-Fi turned on. Additionally, IBM's Sterling Store Engagement solution provides a real-time view of store inventory, and order data through an intuitive user interface that can be accessed by store owners from counters and mobile devices.

Heavy investments in healthcare sectors, advancements in technologies to help manage a large number of medical records, and the use of Big Data analytics to efficiently predict at-risk patients and create effective treatment plans are further contributing to the growth of the predictive analytics market in North America. Predictive analytics helps assess patterns in a patients' medical records, thereby allowing healthcare professionals to develop effective treatment plans to Excellerate outcomes. During the COVID-19 pandemic, healthcare predictive analytics solutions helped provide hospitals with insightful predictions of the number of hospitalizations for various treatments, which significantly helped them deal with the influx of a large number of patients. However, the high costs of installation and a shortage of skilled workers may limit the use of predictive analytics solutions in, both, the retail and healthcare sectors.

Browse Adjoining Reports:

Procurement Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Application (Supply Chain Analytics, Risk Analytics, Spend Analytics, Demand Forecasting, Contract Management, Vendor Management); Deployment (Cloud, On Premises); Industry Vertical (Retail and E Commerce, Manufacturing, Government and Defense, Healthcare and Life sciences, Telecom and IT, Energy and Utility, Banking Financial Services and Insurance) and Geography

Risk Analytics Market Forecast to 2028 - Covid-19 Impact and Global Analysis - by Component (Software, Services); Type (Strategic Risk, Financial Risk, Operational Risk, Others); Deployment Mode (Cloud, On-Premise); Industry Vertical (BFSI, IT and Telecom, Manufacturing, Retail and Consumer Goods, Transportation and Logistics, Government and Defense, Energy and Utilities, Healthcare and Life Sciences, Others) and Geography

Preventive Risk Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component (Solution, Services); Deployment Type (On-Premise, Cloud); Organization Size (SMEs, Large Enterprises); Type (Strategic Risks, Financial Risks, Operational Risks, Compliance Risks); Industry (BFSI, Energy and Utilities, Government and Defense, Healthcare, Manufacturing, IT and Telecom, Retail, Others) and Geography

Business Analytics Market Forecast to 2028 - Covid-19 Impact and Global Analysis - by Application (Supply Chain Analytics, Spatial Analytics, Workforce Analytics, Marketing Analytics, Behavioral Analytics, Risk And Credit Analytics, and Pricing Analytics); Deployment (On-Premise, Cloud, and Hybrid); End-user (BFSI, IT & Telecom, Manufacturing, Retail, Energy & Power, and Healthcare)

Big Data Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component (Software and Services), Analytics Tool (Dashboard and Data Visualization, Data Mining and Warehousing, Self-Service Tool, Reporting, and Others), Application (Customer Analytics, Supply Chain Analytics, Marketing Analytics, Pricing Analytics, Workforce Analytics, and Others), and End Use Industry (Pharmaceutical, Semiconductor, Battery Manufacturing, Electronics, and Others)

Data Analytics Outsourcing Market to 2027 - Global Analysis and Forecasts by Type (Descriptive Data Analytics, Predictive Data Analytics, and Prescriptive Data Analytics); Application (Sales Analytics, Marketing Analytics, Risk & Finance Analytics, and Supply Chain Analytics); and End-user (BFSI, Healthcare, Retail, Manufacturing, Telecom, and Media & Entertainment)

Sales Performance Management Market Forecast to 2028 - Covid-19 Impact and Global Analysis - by Solution (Incentive Compensation Management, Territory Management, Sales Monitoring and Planning, and Sales Analytics), Deployment Type (On-premise, Cloud), Services (Professional Services, Managed Services), End User (BFSI, Manufacturing, Energy and Utility, and Healthcare)

Customer Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component (Solution, Services); Deployment Type (On-premises, Cloud); Enterprise Size (Small and Medium-sized Enterprises, Large Enterprises); End-user (BFSI, IT and Telecom, Media and Entertainment, Consumer Goods and Retail, Travel and Hospitality, Others) and Geography

Life Science Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Type (Predictive Analytics, Prescriptive Analytics, Descriptive Analytics); Component (Services, Software); End User (Pharmaceutical & Biotechnology Companies, Research Centers, Medical Device Companies, Third-Party Administrators)

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Killexams : UTC And IBM Help Students Develop Job Skills For The Future

A new collaboration between The University of Tennessee at Chattanooga and IBM will deliver students an array of marketable skills in data analytics, one of the fastest growing sectors in the national and regional economy. Smart technologies, cloud computing, mobile platforms, social media and other new generation technologies are fueling the revolution of big data.

According to IBM’s recent Tech Trends Report, only 1 in 10 organizations has the skills needed to effectively apply advanced technologies such as business analyticsmobile computingcloud computing and social business.

In the U.S. alone, IT jobs are expected to grow by 22 percent through 2020 according to the US Bureau of Labor Statistics.

As part of the UTC-IBM relationship, faculty have access to the resources in IBM’s Academic Initiative. This program provides access to technology, curriculum and learning materials at no cost for faculty members around the world. IBM has also provided advanced education for UTC faculty members, which has resulted in the development of curriculum, according to Dr. Joseph Kizza, Professor and Head of the Department of Computer Science and Engineering. Kizza is currently teaching the course Big Data Analytics using IBM software, including Cognos, BigInsights and InfoSphereStreams.

“The collaboration with IBM is already helping the program to prepare graduating students with the most up-to-date and in-demand skills in the industry. This is going to help our students get better and more paying jobs. In the process, this will eventually help our recruitment,” explained Kizza.  “Additionally, we hold two Computer Science and Engineering (CSE) Showcases a year where we invite high school students to spend a Saturday with us and do hands-on labs. This opportunity will help us demonstrate the latest technologies in Big Data analytics, for example, which may increase or ignite students interests in attending UTC.”

BlueCross BlueShield of Tennessee, the city’s largest private employer, also collaborated by providing guidance on the curriculum and sharing insights on the real-world skills needed for success. The company believes the program will not only help meet its own need for technical talent, but provide a boost for the entire community.

“I see UTC becoming a leading academic institution for business analysis and data management—it will begin to produce the business intelligence experts Chattanooga companies need,” according to Brian Green, Manager of Business Intelligence and Performance Management at BlueCross. “Through this collaboration, local businesses will be able to tap into UTC’s academic offerings to make their companies more successful in critical new areas like Big Data analytics.”

He says students who take advantage of these opportunities can become “immediately marketable” in this fast-growing field. “They will graduate with hands-on experience in the IBM tools commonly used in the workplace.”

 

Mr.Green, who graduated from UTC in 1980, has been in the business of information management and system development in the insurance industry for more than 30 years.

“UTC has been involved in the IBM Academic Skills Cloud pilot program and we are proud to support the university’s efforts to provide students with data-driven education,” said Dan Hauenstein, director, IBM Academic Initiative. “Through the IBM Academic Initiative, students and faculty have access to industry leading technology and courseware to help develop the advanced big data analytics skills needed for jobs of the future.”

IBM’s Academic Initiative provides no charge access to curriculum, software and learning materials to more than 30,000 faculty members around the world. 

Tue, 12 Jul 2022 12:00:00 -0500 en text/html https://www.chattanoogan.com/2013/10/12/261192/UTC-And-IBM-Help-Students-Develop-Job.aspx
Killexams : SIMPLILEARN COLLABORATES WITH THE UNIVERSITY OF MINNESOTA'S CARLSON SCHOOL OF MANAGEMENT TO LAUNCH BUSINESS ANALYTICS BOOTCAMP
  • The program is best suited for early-mid career professionals with 2+ years of work experience.

  • Upon program completion, learners will receive a certificate from Carlson School of Management and Simplilearn.

  • The program includes masterclasses delivered by distinguished faculty from the University of Minnesota and industry experts from IBM.

  • The bootcamp will also include career assistance from Simplilearn.

SAN FRANCISCO, Aug. 2, 2022 /PRNewswire/ -- Simplilearn, a global digital skills training provider, announced its partnership with the University of Minnesota's Carlson School of Management for a Bootcamp in Business Analytics. The program will provide a well-planned, high-level understanding of business analytics and the real-world application of analytics across multiple domains. Anyone who has completed their bachelor's degree (in any background) is eligible for this program. The bootcamp is best suited for early-mid career professionals having 2+ years of formal work experience, such as IT professionals, Banking and Finance professionals, Marketing Managers, Supply Chain Network Managers, Analysts, and Consultants.

Simplilearn_Logo

The six-month program will be based on a blended format of online self-learning and live virtual classes. The key features of the program include:

  • Program completion certificate from the Carlson School of Management and Simplilearn

  • Membership to University of Minnesota's Alumni Association

  • Masterclasses delivered by Carlson School of Management faculty and industry experts from IBM

  • Industry-recognized IBM certificates for IBM courses

  • Ask-Me-Anything sessions & hackathons conducted by IBM

  • More than twelve industry-relevant projects

  • Simplilearn career assistance

  • Integrated labs

The program curriculum consists of R Programming for Data Science, SQL, Business Analytics with Excel, Data Analytics with Python, Capstone Projects, and other modules.

Speaking about the program, Mr. Anand Narayanan, Chief Product Officer, Simplilearn, said, "In today's business-driven environment, every organization is devising ways to make their decision-making more insightful and impactful. As a result, the role of business analytics continues to grow in importance. Business Analytics provides companies the ability to derive deeper insights and create stronger business recommendations for their own success. Given the industry relevance of the program, we have partnered with the University of Minnesota's Carlson School of Management to curate this Business Analytics Bootcamp that will provide learners an extensive knowledge of the Topic and widen growth and professional opportunities."

Speaking on the partnership with Simplilearn, Dr. Ravi Bapna, Curtis L. Carlson Chair in Business Analytics and Information Systems at the Carlson School of Management, University of Minnesota said, "Business Analytics can help companies make better, more informed decisions and achieve various goals. Its ability to navigate crises has further increased its importance to the business. Business analytics has certainly changed the dynamics of working in the digital economy and how companies operate."

Simplilearn conducts more than 3000 live classes, with an average of 70,000 learners who together spend more than 500,000 hours each month on the platform. Simplilearn's programs allow learners to upskill and get certified in popular domains. In 2020, Simplilearn introduced a free skills development program called SkillUp. SkillUp lets learners explore in-demand subjects in top professional and technology fields for free, helping them make the right learning and career decisions.

About the Carlson School of Management, University of Minnesota

The Curtis L. Carlson School of Management at the University of Minnesota is a recognized leader in business education and research. Established in 1919 and located in Minneapolis, the Carlson School is committed to developing leaders who believe business is a force for good through experiential learning, international education, and the school's strong ties to the dynamic Twin Cities business community.

With 13 degree programs that are ranked consistently among the world's best, the school offers bachelor's, master's, and doctoral degrees, as well as executive education programs hosted both domestically and abroad. Today, the Carlson School has nearly 60,000 alumni in more than 100 countries.

About Simplilearn

Founded in 2010 and based in San Francisco, California, and Bangalore, India, Simplilearn, a Blackstone company is the world's #1 online Bootcamp provider for digital economy skills training. Simplilearn offers access to world-class work-ready training to individuals and businesses around the world. The Bootcamps are designed and delivered with world-renowned universities, top corporations, and leading industry bodies via live online classes featuring top industry practitioners, sought-after trainers, and global leaders. From college students and early career professionals to managers, executives, small businesses, and big corporations, Simplilearn role-based, skill-focused, industry-recognized, and globally relevant training programs are ideal upskilling solutions for diverse career or/and business goals.

For more information, please visit www.simplilearn.com/

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Killexams : IBM adds four servers to Power10 lineup

IBM is expanding its Power10 server lineup with four new midrange and scale-out systems designed for on-premises, data-intensive and business-critical workloads.

The new Power S1014, Power S1022, Power S1024 and Power E1050 platforms cover a range of workloads.

The E1050 is a four-socket system optimized for data-intensive enterprise workloads. In terms of how it ranks, the E1050 is a step below the top end of the Power10 portfolio, which is the existing E1080 four-socket rack server.

The new scale-out systems are the single-socket S1014, described as ideal for entry-level SMBs and remote offices, and the S1022 and S1024 systems, which are two-socket systems aimed at higher-end uses.

The Power S1022 scale-out server is optimized for cloud-native, containerized environments, while the S1024 targets the data analytics space and high-end apps like SAP/HANA. Both servers use transparent memory encryption, enhanced isolation and Trusted Boot to help prevent emerging side-channel attacks without impacting application performance.

Power10 is a different design from x86. It comes with 15 SMT8 cores, meaning each of the 15 cores has eight threads. This gives a single Power10 CPU effectively 120 threads.

Copyright © 2022 IDG Communications, Inc.

Mon, 18 Jul 2022 07:11:00 -0500 en text/html https://www.networkworld.com/article/3667472/ibm-adds-four-servers-to-power10-lineup.html
Killexams : IBM Teams Up With Box to Make It Easier to Work in the Cloud No result found, try new keyword!In 2014, IBM's revenue derived from cloud accounted for $7 billion, up 60% year over year. The partnership will focus on three areas -- content management, Watson Analytics and bringing Box's ... Fri, 22 Jul 2022 12:00:00 -0500 en-us text/html https://www.thestreet.com/technology/ibm-teams-up-with-box-to-make-it-easier-to-work-in-the-cloud-13196989 Killexams : Taking Predictive Maintenance from the IIoT to Big Data Analytics

Predictive maintenance can grow far beyond traditional condition monitoring when the data from equipment is gathered through the Industrial Internet of Things (IIoT) and then stored and processed through Big Data analysis systems, such as IBM’s Watson. “When you gather the regular maintenance data, you can build a history of the data. Then you use algorithms to detect anomalous behavior in the historical data. In time, you learn that when you see this anomaly, you know—based on the history—that this component is likely to fail in the next 10 to 17 days,” Tom Craven, VP of product strategy at RRAMAC Connected Systems, told Design News. “The analysis of the data can predict the very specific failures in a specific timeframe. That’s where IBM Watson comes in.”

Craven will present a session at the Atlantic Design and Manufacturing Show in New York City on June 14 with Kayed Almasarweh, the Watson and cognitive IoT solutions lead at IBM. The program, Leveraging IoT for Predictive Maintenance, will look at the combination of condition monitoring data collection and the analysis of that data via Big Data processing in IBM’s Watson.

Grabbing the Quick ROI

Before customers make the major jump into Big Data processing, they can enjoy an early return on investment (ROI) from predictive maintenance basics—the stream of equipment data that comes from sensors and is delivered to a condition monitoring system via the IIoT. ROI can be achieved more quickly if the company doesn’t have to set up the servers and configure the software. Service companies like RRAMAC can grab the sensor data over the internet and process it on remote servers. “When you’re not installing a bunch of software and spending time learning how to configure it in-house, it shortens the timeline to the ROI,” said Craven. “The initial investment is less when you don’t have to invest in all the development hours to get it going.”

The reduced investment allows companies that couldn’t otherwise afford to develop a condition monitoring system to reap the benefits of predictive maintenance. “The IIoT brings predictive maintenance to a whole new set of customers where it wouldn’t have made sense before,” said Craven. “A lot of companies can benefit from predictive maintenance even if it doesn’t make sense for them to do it on their own.”

Giving the OEMs Their Own Equipment Data

Not all predictive maintenance data needs to go directly to the end user. In some cases, the end customer is using a piece of equipment that’s not connected to a factory line. Examples can include a recycling machine or a rock crusher. The user doesn’t have the network to gather equipment data, so the equipment OEM can track the machine data and monitor the equipment’s health. “Sometimes, our customer is the OEM. The OEM gets the information. The customer may get the information as well, including the alerts,” said Craven. “We provide data to the OEM and if the OEM chooses, the OEM can provide the data to the customer.”

OEMs often sell extended warranties. But the OEM can only sell the extended warranty if the health of the machine can be monitored. “If you have a machine that requires maintenance, those machines can wear out quickly if they’re not maintained,” said Craven. “If the OEM is monitoring the equipment regularly and making sure the customer is doing regular maintenance, the OEM can extend the warranty knowing that it’s enforced. It’s just like Ford not supporting the warranty on a car that hasn’t had regular oil changes.”

Rob Spiegel has covered automation and control for 17 years, 15 of them for Design News. Other subjects he has covered include supply chain technology, alternative energy, and cyber security. For 10 years, he was owner and publisher of the food magazine Chile Pepper.

Wed, 20 Jul 2022 12:00:00 -0500 en text/html https://www.designnews.com/automation-motion-control/taking-predictive-maintenance-iiot-big-data-analytics
Killexams : Taking Predictive Maintenance into Analytics

Predictive maintenance delivers measurable benefits in its ability to reveal equipment weakness before failure. Analyzing predictive maintenance data can deliver even greater benefits in the ability to optimize plant equipment and processes. It can offer pathways to KPI improvements. Analytics can also determine what solutions are required when equipment begins to fail.

At the Atlantic Design and Manufacturing Show in New York City last month, Tom Craven, VP of product strategy at RRAMAC Connected Systems, and Kayed Almasarweh, Watson and cognitive IoT solutions lead at IBM, explained both the process of gathering plant data and how the analysis of that data can Excellerate plant systems.

The one-two punch of Craven and Almasarweh offered a strong program that explained the possibilities of predictive maintenance when its data is pushed into analytics. Craven has long been one of Design News’ top sources—both in feature stories and as a speaker at conferences. Almasarweh brings the additional perspective of IBM Watson’s analytics. IBM has long been ranked among the top companies in predictive maintenance analytics.

This session, Leveraging IoT for Predictive Maintenance, draws on the strengths of both experts. It covers the benefits of tracking equipment via IoT data gathering and analyzing the resulting data in order to optimize plant systems.

Rob Spiegel has covered automation and control for 17 years, 15 of them for Design News. Other subjects he has covered include supply chain technology, alternative energy, and cyber security. For 10 years, he was owner and publisher of the food magazine Chile Pepper.

Sun, 17 Jul 2022 11:59:00 -0500 en text/html https://www.designnews.com/automation-motion-control/taking-predictive-maintenance-analytics
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