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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 Strengthen 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 Strengthen 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 Strengthen 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 : Top 10 data lake solution vendors in 2022

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As the world becomes increasingly data-driven, businesses must find suitable solutions to help them achieve their desired outcomes. Data lake storage has garnered the attention of many organizations that need to store large amounts of unstructured, raw information until it can be used in analytics applications.

The data lake solution market is expected to grow rapidly in the coming years and is driven by vendors that offer cost-effective, scalable solutions for their customers.

Learn more about data lake solutions, what key features they should have and some of the top vendors to consider this year. 

What is a data lake solution?

A data lake is defined as a single, centralized repository that can store massive amounts of unstructured and semi-structured information in its native, raw form. 

It’s common for an organization to store unstructured data in a data lake if it hasn’t decided how that information will be used. Some examples of unstructured data include images, documents, videos and audio. These data types are useful in today’s advanced machine learning (ML) and advanced analytics applications.

Data lakes differ from data warehouses, which store structured, filtered information for specific purposes in files or folders. Data lakes were created in response to some of the limitations of data warehouses. For example, data warehouses are expensive and proprietary, cannot handle certain business use cases an organization must address, and may lead to unwanted information homogeneity.

On-premise data lake solutions were commonly used before the widespread adoption of the cloud. Now, it’s understood that some of the best hosts for data lakes are cloud-based platforms on the edge because of their inherent scalability and considerably modular services. 

A 2019 report from the Government Accountability Office (GAO) highlights several business benefits of using the cloud, including better customer service and the acquisition of cost-effective options for IT management services.

Cloud data lakes and on-premise data lakes have pros and cons. Businesses should consider cost, scale and available technical resources to decide which type is best.

Read more about data lakes: What is a data lake? Definition, benefits, architecture and best practices

5 must-have features of a data lake solution

It’s critical to understand what features a data lake offers. Most solutions come with the same core components, but each vendor may have specific offerings or unique selling points (USPs) that could influence a business’s decision.

Below are five key features every data lake should have:

1. Various interfaces, APIs and endpoints

Data lakes that offer diverse interfaces, APIs and endpoints can make it much easier to upload, access and move information. These capabilities are important for a data lake because it allows unstructured data for a wide range of use cases, depending on a business’s desired outcome.

2. Support for or connection to processing and analytics layers

ML engineers, data scientists, decision-makers and analysts benefit most from a centralized data lake solution that stores information for easy access and availability. This characteristic can help data professionals and IT managers work with data more seamlessly and efficiently, thus improving productivity and helping companies reach their goals.

3. Robust search and cataloging features

Imagine a data lake with large amounts of information but no sense of organization. A viable data lake solution must incorporate generic organizational methods and search capabilities, which provide the most value for its users. Other features might include key-value storage, tagging, metadata, or tools to classify and collect subsets of information.

4. Security and access control

Security and access control are two must-have features with any digital tool. The current cybersecurity landscape is expanding, making it easier for threat actors to exploit a company’s data and cause irreparable damage. Only certain users should have access to a data lake, and the solution must have strong security to protect sensitive information.

5. Flexibility and scalability

More organizations are growing larger and operating at a much faster rate. Data lake solutions must be flexible and scalable to meet the ever-changing needs of modern businesses working with information.

Also read: Unlocking analytics with data lake and graph analysis

Top 10 data lake solution vendors in 2022

Some data lake solutions are best suited for businesses in certain industries. In contrast, others may work well for a company of a particular size or with a specific number of employees or customers. This can make choosing a potential data lake solution vendor challenging. 

Companies considering investing in a data lake solution this year should check out some of the vendors below.

1. Amazon Web Services (AWS)

The AWS Cloud provides many essential tools and services that allow companies to build a data lake that meets their needs. The AWS data lake solution is widely used, cost-effective and user-friendly. It leverages the security, durability, flexibility and scalability that Amazon S3 object storage offers to its users. 

The data lake also features Amazon DynamoDB to handle and manage metadata. AWS data lake offers an intuitive, web-based console user interface (UI) to manage the data lake easily. It also forms data lake policies, removes or adds data packages, creates manifests of datasets for analytics purposes, and features search data packages.

2. Cloudera

Cloudera is another top data lake vendor that will create and maintain safe, secure storage for all data types. Some of Cloudera SDX’s Data Lake Service capabilities include:

  • Data schema/metadata information
  • Metadata management and governance
  • Compliance-ready access auditing
  • Data access authorization and authentication for improved security

Other benefits of Cloudera’s data lake include product support, downloads, community and documentation. GSK and Toyota leveraged Cloudera’s data lake to garner critical business intelligence (BI) insights and manage data analytics processes.

3. Databricks 

Databricks is another viable vendor, and it also offers a handful of data lake alternatives. The Databricks Lakehouse Platform combines the best elements of data lakes and warehouses to provide reliability, governance, security and performance.

Databricks’ platform helps break down silos that normally separate and complicate data, which frustrates data scientists, ML engineers and other IT professionals. Aside from the platform, Databricks also offers its Delta Lake solution, an open-format storage layer that can Strengthen data lake management processes. 

4. Domo

Domo is a cloud-based software company that can provide big data solutions to all companies. Users have the freedom to choose a cloud architecture that works for their business. Domo is an open platform that can augment existing data lakes, whether it’s in the cloud or on-premise. Users can use combined cloud options, including:

  • Choosing Domo’s cloud
  • Connecting to any cloud data
  • Selecting a cloud data platform

Domo offers advanced security features, such as BYOK (bring your own key) encryption, control data access and governance capabilities. Well-known corporations such as Nestle, DHL, Cisco and Comcast leverage the Domo Cloud to better manage their needs.

5. Google Cloud

Google is another big tech player offering customers data lake solutions. Companies can use Google Cloud’s data lake to analyze any data securely and cost-effectively. It can handle large volumes of information and IT professionals’ various processing tasks. Companies that don’t want to rebuild their on-premise data lakes in the cloud can easily lift and shift their information to Google Cloud. 

Some key features of Google’s data lakes include Apache Spark and Hadoop migration, which are fully managed services, integrated data science and analytics, and cost management tools. Major companies like Twitter, Vodafone, Pandora and Metro have benefited from Google Cloud’s data lakes.

6. HP Enterprise

Hewlett Packard Enterprise (HPE) is another data lake solution vendor that can help businesses harness the power of their big data. HPE’s solution is called GreenLake — it offers organizations a truly scalable, cloud-based solution that simplifies their Hadoop experiences. 

HPE GreenLake is an end-to-end solution that includes software, hardware and HPE Pointnext Services. These services can help businesses overcome IT challenges and spend more time on meaningful tasks. 

7. IBM

Business technology leader IBM also offers data lake solutions for companies. IBM is well-known for its cloud computing and data analytics solutions. It’s a great choice if an operation is looking for a suitable data lake solution. IBM’s cloud-based approach operates on three key principles: embedded governance, automated integration and virtualization.

These are some data lake solutions from IBM: 

  • IBM Db2
  • IBM Db2 BigSQL
  • IBM Netezza
  • IBM Watson Query
  • IBM Watson Knowledge Catalog
  • IBM Cloud Pak for Data

With so many data lakes available, there’s surely one to fit a company’s unique needs. Financial services, healthcare and communications businesses often use IBM data lakes for various purposes.

8. Microsoft Azure

Microsoft offers its Azure Data Lake solution, which features easy storage methods, processing, and analytics using various languages and platforms. Azure Data Lake also works with a company’s existing IT investments and infrastructure to make IT management seamless.

The Azure Data Lake solution is affordable, comprehensive, secure and supported by Microsoft. Companies benefit from 24/7 support and expertise to help them overcome any big data challenges they may face. Microsoft is a leader in business analytics and tech solutions, making it a popular choice for many organizations.

9. Oracle

Companies can use Oracle’s Big Data Service to build data lakes to manage the influx of information needed to power their business decisions. The Big Data Service is automated and will provide users with an affordable and comprehensive Hadoop data lake platform based on Cloudera Enterprise. 

This solution can be used as a data lake or an ML platform. Another important feature of Oracle is it is one of the best open-source data lakes available. It also comes with Oracle-based tools to add even more value. Oracle’s Big Data Service is scalable, flexible, secure and will meet data storage requirements at a low cost.

10. Snowflake

Snowflake’s data lake solution is secure, reliable and accessible and helps businesses break down silos to Strengthen their strategies. The top features of Snowflake’s data lake include a central platform for all information, fast querying and secure collaboration.

Siemens and Devon Energy are two companies that provide testimonials regarding Snowflake’s data lake solutions and offer positive feedback. Another benefit of Snowflake is its extensive partner ecosystem, including AWS, Microsoft Azure, Accenture, Deloitte and Google Cloud.

The importance of choosing the right data lake solution vendor 

Companies that spend extra time researching which vendors will offer the best enterprise data lake solutions for them can manage their information better. Rather than choose any vendor, it’s best to consider all options available and determine which solutions will meet the specific needs of an organization.

Every business uses information, some more than others. However, the world is becoming highly data-driven — therefore, leveraging the right data solutions will only grow more important in the coming years. This list will help companies decide which data lake solution vendor is right for their operations.

Read next: Get the most value from your data with data lakehouse architecture

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Learn more about membership.

Fri, 15 Jul 2022 07:14:00 -0500 Shannon Flynn en-US text/html https://venturebeat.com/2022/07/15/top-10-data-lake-solution-vendors-in-2022/
Killexams : A degree in data science is not important – Debdoot Mukherjee, Head of AI, Meesho

Debdoot Mukherjee is the Chief Data Scientist and Head of AI at Meesho, the Indian origin social commerce platform at the forefront of the boundaryless workplace model that became a norm in the aftermath of the Covid-19 pandemic. Upon completing his postgraduate degree from IIT-Delhi, Mukherjee began his career in the research division at IBM, where he attained expertise in Information Retrieval and Machine Learning techniques. He then journeyed on to work in impactful roles at companies like Hike, Myntra and ShareChat before leading the AI and data science division at Meesho. 

In an exclusive interview with Analytics India Magazine, Debdoot Mukherjee opened up about his journey into data science, machine learning and everything AI. 

AIM: What attracted you to this field?

Debdoot: My first brush with machine learning was during my masters where I took a few courses related to the subject. As I progressed, my interest in the field kept growing. Post graduation, I joined IBM research where I got a chance to go deep into new technologies. It became a routine where machine learning was turning out to be a great tool to apply in every project. In the last decade, progress in the field of AI/ML has trumped all of us. Later when I moved to Myntra, I got the opportunity to apply all the techniques that I’d learnt to achieve significant results. That’s what keeps me going in this field.

AIM: Would you say holding a degree in data science/AI is enough?

Debdoot: Machine learning is a field where theoretical knowledge is very important. Awareness of the right state-of-the-art ML techniques and knowing how to implement them on the problem statement requires a great deal of clarity on the theoretical foundations of the subject. So, from the standpoint of formal training, a degree does not seem important. However, it is of paramount importance that the foundations are clear, which then comes from proper college training. After gaining theoretical knowledge, the next step is to understand the practical applications, which comes with hands-on projects, hackathons and such. Practicing these techniques as part of an industry or academia provides a broad perspective on applications which result in out of the box solutions. 

AIM: With so many patents to your name, how were you able to come up with such ideas?

Debdoot: It is all part and parcel of working in a research lab. The goal of researchers is to look for and develop ideas that have a significant impact. One is also expected to drive this impact in both the business world and academic world. Overtime, one does get a playbook on how to convert ideas into patents. 

AIM: How does Meesho leverage AI/ML in its business ?

Debdoot: The mission statement of Meesho is to use AI/ML as a sort of enabler to all pillars of e-commerce platforms, marketplace trends, and such. There are a lot of applications on the demand side, like the consumer side where people discover products—be it the feed that one landed on, or opening a different category listing pages on an app or sifting through the search interface itself—AI is being integrated in features like computer vision, virtual assistants, search enablement to Strengthen the user experience. We are also working on the preempt mechanism and, with time and history of user preferences, we will be able to recommend certain products that a user will need in the future. However, a lot of this is serendipitous discovery, where, based on the depth of understanding, the user can be recommended a lot of products, without having a clear shopping intent in that category. Now from the supply side, the scenario is not that different. A lot of applications are largely led by recommendation systems and ranking monitors on a variety of touch points. 

AIM: Your vision for the future?

Debdoot: In this day and age, AI has become a prerequisite for a successful business as a major part of the business process has AI/ML techniques integrated into them. However, there are many other industries where AI adoption is still in its infancy. Artificial intelligence has the power to not only transform businesses but also society at large. AI can do well in some variability of large and structured data sets but it struggles to replicate intuition. Natural Language Processing, object detection and image generation are some of the challenges that research institutes and scientists are working to crack. My vision is that AI/ML models create solutions that humans can utilise in various tasks, but not replace humans in any manner.

AIM: What is your point of view on AGI? Have we achieved it yet?

Debdoot: I’m pretty sure that we haven’t achieved it yet. However, sentience in its essence is fairly subjective, like emotion, perception and so on. AI has not reached the level of human intelligence as a lot of these machines still fall short in comparison to the human brain. Keeping that in mind, the next phase of development is mimicking the workings of the human brain. The metric might not be the same and for most cases, AI requires a lot of data, pre-conditions, and such. One must look into nature for answers. The solution is natural and causal. So far, the end result has been very good. But, we need to fundamentally change the approach and then you can think of getting closer to AGI.

Wed, 27 Jul 2022 22:34:00 -0500 en-US text/html https://analyticsindiamag.com/a-degree-in-data-science-is-not-important-debdoot-mukherjee-head-of-ai-meesho/
Killexams : CIOReview Names Cobalt Iron Among 10 Most Promising IBM Solution Providers 2022

Cobalt Iron Inc., a leading provider of SaaS-based enterprise data protection, today announced that the company has been deemed one of the 10 Most Promising IBM Solution Providers 2022 by CIOReview Magazine. The annual list of companies is selected by a panel of experts and members of CIOReview Magazine's editorial board to recognize and promote innovation and entrepreneurship. A technology partner for IBM, Cobalt Iron earned the distinction based on its Compass® enterprise SaaS backup platform for monitoring, managing, provisioning, and securing the entire enterprise backup landscape.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20220728005043/en/

Cobalt Iron Compass® is a SaaS-based data protection platform leveraging strong IBM technologies for delivering a secure, modernized approach to data protection. (Graphic: Business Wire)

Cobalt Iron Compass® is a SaaS-based data protection platform leveraging strong IBM technologies for delivering a secure, modernized approach to data protection. (Graphic: Business Wire)

According to CIOReview, "Cobalt Iron has built a patented cyber-resilience technology in a SaaS model to alleviate the complexities of managing large, multivendor setups, providing an effectual humanless backup experience. This SaaS-based data protection platform, called Compass, leverages strong IBM technologies. For example, IBM Spectrum Protect is embedded into the platform from a data backup and recovery perspective. ... By combining IBM's technologies and the intellectual property built by Cobalt Iron, the company delivers a secure, modernized approach to data protection, providing a 'true' software as a service."

Through proprietary technology, the Compass data protection platform integrates with, automates, and optimizes best-of-breed technologies, including IBM Spectrum Protect, IBM FlashSystem, IBM Red Hat Linux, IBM Cloud, and IBM Cloud Object Storage. Compass enhances and extends IBM technologies by automating more than 80% of backup infrastructure operations, optimizing the backup landscape through analytics, and securing backup data, making it a valuable addition to IBM's data protection offerings.

CIOReview also praised Compass for its simple and intuitive interface to display a consolidated view of data backups across an entire organization without logging in to every backup product instance to extract data. The mahine learning-enabled platform also automates backup processes and infrastructure, and it uses open APIs to connect with ticket management systems to generate tickets automatically about any backups that need immediate attention.

To ensure the security of data backups, Cobalt Iron has developed an architecture and security feature set called Cyber Shield for 24/7 threat protection, detection, and analysis that improves ransomware responsiveness. Compass is also being enhanced to use several patented techniques that are specific to analytics and ransomware. For example, analytics-based cloud brokering of data protection operations helps enterprises make secure, efficient, and cost-effective use of their cloud infrastructures. Another patented technique - dynamic IT infrastructure optimization in response to cyberthreats - offers unique ransomware analytics and automated optimization that will enable Compass to reconfigure IT infrastructure automatically when it detects cyberthreats, such as a ransomware attack, and dynamically adjust access to backup infrastructure and data to reduce exposure.

Compass is part of IBM's product portfolio through the IBM Passport Advantage program. Through Passport Advantage, IBM sellers, partners, and distributors around the world can sell Compass under IBM part numbers to any organizations, particularly complex enterprises, that greatly benefit from the automated data protection and anti-ransomware solutions Compass delivers.

CIOReview's report concludes, "With such innovations, all eyes will be on Cobalt Iron for further advancements in humanless, secure data backup solutions. Cobalt Iron currently focuses on IP protection and continuous R&D to bring about additional cybersecurity-related innovations, promising a more secure future for an enterprise's data."

About Cobalt Iron

Cobalt Iron was founded in 2013 to bring about fundamental changes in the world's approach to secure data protection, and today the company's Compass® is the world's leading SaaS-based enterprise data protection system. Through analytics and automation, Compass enables enterprises to transform and optimize legacy backup solutions into a simple cloud-based architecture with built-in cybersecurity. Processing more than 8 million jobs a month for customers in 44 countries, Compass delivers modern data protection for enterprise customers around the world. www.cobaltiron.com

Product or service names mentioned herein are the trademarks of their respective owners.

Link to Word Doc: www.wallstcom.com/CobaltIron/220728-Cobalt_Iron-CIOReview_Top_IBM_Provider_2022.docx

Photo Link: www.wallstcom.com/CobaltIron/Cobalt_Iron_CIO_Review_Top_IBM_Solution_Provider_Award_Logo.pdf

Photo Caption: Cobalt Iron Compass® is a SaaS-based data protection platform leveraging strong IBM technologies for delivering a secure, modernized approach to data protection.

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Thu, 28 Jul 2022 02:51:00 -0500 text/html https://www.tmcnet.com/usubmit/2022/07/28/9646864.htm
Killexams : IBM report shows cyberattacks growing fast in number, scale No result found, try new keyword!A new report out of IBM shows that when it comes to the rising threat of data breaches, it’s the consumer – not the company – fronting the price tag. Fri, 29 Jul 2022 22:30:00 -0500 text/html https://www.bizjournals.com/triad/news/2022/07/30/ibm-data-cyberattacks-growing-in-number-scale.html Killexams : IBM Touts AI, Hybrid Cloud: ‘Demand For Our Solutions Remains Strong’

Cloud News

Joseph F. Kovar

‘Given its ability to boost innovation, productivity, resilience, and help organizations scale, IT has become a high priority in a company’s budget. As such, there is every reason to believe technology spending in the B2B space will continue to surpass GDP growth,’ says IBM CEO Arvind Krishna.

 ARTICLE TITLE HERE

A strengthening IT environment that is playing into IBM AI and hybrid cloud capabilities means a rosy future for IBM and its B2B business, CEO Arvind Krishna told investors Monday.

Krishna, in his prepared remarks for IBM’s second fiscal quarter 2022 financial analyst conference call, said that technology serves as a fundamental source of competitive advantage for businesses.

“It serves as both a deflationary force and a force multiplier, and is especially critical as clients face challenges on multiple fronts from supply chain bottlenecks to demographic shifts,” he said. “Given its ability to boost innovation, productivity, resilience, and help organizations scale, IT has become a high priority in a company’s budget. As such, there is every reason to believe technology spending in the B2B space will continue to surpass GDP growth.”

[Related: IBM STARTS ‘ORDERLY WIND-DOWN’ OF RUSSIA BUSINESS]

That plays well with IBM’s hybrid cloud and AI strategy where the company is investing in its offerings, technical talent, ecosystem, and go-to-market model, Krishna said.

“Demand for our solutions remains strong,” he said. “We continued to have double-digit performance in IBM Consulting, broad-based strength in software, and with the z16 [mainframe] platform launch, our infrastructure business had a good quarter. By integrating technology and expertise from IBM and our partners, our clients will continue to see our hybrid cloud and AI solutions as a crucial source of business opportunity and growth.”

Krishna said hybrid clouds are about offering clients a platform to straddle multiple public clouds, private clouds, on-premises infrastructures, and the edge, which is where Red Hat, which IBM acquired in 2019, comes into play, Krishna said.

“Our software has been optimized to run on that platform, and includes advanced data and AI, automation, and the security capabilities our clients need,” he said. “Our global team of consultants offers deep business expertise and co-creates with clients to accelerate their digital transformation journeys. Our infrastructure allows clients to take full advantage of an extended hybrid cloud environment.”

As a result, IBM now has over 4,000 hybrid cloud platform clients, with over 250 new clients added during the second fiscal quarter, Krishna said.

“Those who adopt our platform tend to consume more of our solutions across software, consulting, and infrastructure, [and] expanding our footprint within those clients,” he said.

IBM is also benefitting from the steady adoption by businesses of artificial intelligence technologies as those businesses try to process the enormous amount of data generated from hybrid cloud environments all the way to the edge, Krishna said. An IBM study released during the second fiscal quarter found that 35 percent of companies are now using some form of AI with automation in their business to address demographic shifts and move their employees to higher value work, he said.

“This is one of the many reasons we are investing heavily in both AI and automation,” he said. “These investments are paying off.”

IBM is also moving to develop leadership in quantum computing, Krishna said. The company currently has a 127-qubit quantum computer it its cloud, and is committed to demonstrate the first 400-plus-qubit system before year-end as part of its path to deliver a 1,000-plus-qubit system next year and a 4,000-plus-qubit system in 2025, he said.

“One of the implications of quantum computing will be the need to change how information is encrypted,” he said. “We are proud that technology developed by IBM and our collaborators has been selected by NIST (National Institute of Standards and Technology) as the basis of the next generation of quantum-safe encryption protocols.”

IBM during the quarter also move forward in its mainframe technology with the release of its new z16 mainframe, Krishna said.

“The z16 is designed for cloud-native development, cybersecurity resilience, [and] quantum-safe encryption, and includes an on-chip AI accelerator, which allows clients to reduce fraud within real-time transactions,” he said.

IBM also made two acquisitions during the quarter related to cybersecurity, Krishna said. The first was Randori, an attack surface management and offensive cybersecurity provider. That acquisition built on IBM’s November acquisition of ReaQta, an endpoint security firm, he said.

While analysts during the question and answer part of Monday’s financial analyst conference call did not ask about the news that IBM has brought in Matt Hicks as the new CEO of Red Hat, they did seem concerned about how the 17-percent growth in Red Had revenue over last year missed expectations.

When asked about Red Hat revenue, Krishna said IBM feels very good about the Red Hat business and expect continued strong demand.

“That said, we had said late last year that we expect growth in Red Hat to be in the upper teens,” he said. “That expectation is what we are going to continue with. … Deferred revenue accounts for the bulk of what has been the difference in the growth rates coming down from last year to this year.”

IBM CFO James Kavanaugh followed by saying that while IBM saw 17 percent growth overall for Red Hat, the company took market share with its core REL (Red Hat Enterprise Linux) and in its Red Hat OpenShift hybrid cloud platform foundation. Red Hat OpenShift revenue is now four-and-a-half times the revenue before IBM acquired Red Hat, and Red Hat OpenShift bookings were up over 50 percent, Kavanaugh said.

“So we feel pretty good about our Red Hat portfolio overall. … Remember, we‘re three years into this acquisition right now,” he said. “And we couldn’t be more pleased as we move forward.”

When asked about the potential impact from an economic downturn, Krishna said IBM’s pipelines remain healthy and consistent with what the company saw in the first half of fiscal 2022, making him more optimistic than many of his peers.

“In an inflationary environment, when clients take our technology, deploy it, leverage our consulting, it acts as a counterbalance to all of the inflation and all of the labor demographics that people are facing all over the globe,” he said.

Krishna also said IBM’s consulting business is less likely than most vendors’ business to be impacted by the economic cycle as it involves a lot of work around deploying the kinds of applications critical to clients’ need to optimize their costs. Furthermore, he said. Because consulting is very labor-intensive, it is easy to hire or let go tens of thousands of employees as needed, he said.

The Numbers

For its second fiscal quarter 2022, which ended June 30, IBM reported total revenue of $15.5 billion, up about 9 percent from the $14.2 billion the company reported for its second fiscal quarter 2021.

This includes software revenue of $6.2 billion, up from $5.9 billion; consulting revenue of $4.8 billion, up from $4.4 billion; infrastructure revenue of $4.2 billion, up from $3.6 billion; financing revenue of $146 million, down from $209 million; and other revenue of $180 million, down from $277 million.

On the software side, IBM reported annual recurring revenue of $12.9 billion, which was up 8 percent over last year. Software revenue from its Red Hat business was up 17 percent over last year, while automation software was up 8 percent, data and AI software up 4 percent, and security software up 5 percent.

On the consulting side, technology consulting revenue was up 23 percent over last year, applications operations up 17 percent, and business transformation up 16 percent.

Infrastructure revenue growth was driven by hybrid infrastructure sales, which rose 7 percent over last year, and infrastructure support, which grew 5 percent. Hybrid infrastructure revenue saw a significant boost from zSystems mainframe sales, which rose 77 percent over last year.

IBM also reported revenue of $8.1 billion from sales to the Americas, up 15 percent over last year; sales to Europe, Middle East, and Africa of $4.5 billion, up 17 percent; and $2.9 billion to the Asia Pacific area, up 16 percent.

Sales to Kyndryl, which late last year was spun out of IBM, accounted for about 5 percent of revenue, including 3 percent of IBM’s Americas revenue.

IBM also reported net income for the quarter on a GAAP basis of $1.39 billion, or $1.53 per share, up from last year’s $1.33 billion, or $1.47 per share.

Joseph F. Kovar

Joseph F. Kovar is a senior editor and reporter for the storage and the non-tech-focused channel beats for CRN. He keeps readers abreast of the latest issues related to such areas as data life-cycle, business continuity and disaster recovery, and data centers, along with related services and software, while highlighting some of the key trends that impact the IT channel overall. He can be reached at jkovar@thechannelcompany.com.

Tue, 19 Jul 2022 13:17:00 -0500 en text/html https://www.crn.com/news/cloud/ibm-touts-ai-hybrid-cloud-demand-for-our-solutions-remains-strong-
Killexams : Cognitive Search Tools Market 2022 Supply-Demand, Industry Research and End User Analysis Forecasts 2030

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

Jul 29, 2022 (Alliance News via COMTEX) -- New York (US) - Key Companies Covered in the Cognitive Search Tools Market Research are Attivo, Microsoft, Lucidworks, Coveo, Micro Focus, IBM, Sinequa, Mindbreeze, Squirro and other key market players.

The global Cognitive Search Tools Market is expected to reach US$ Million by 2027, with a CAGR of $$% from 2020 to 2027, based on Report Ocean newly published report. The demand for Internet-of-Things (IoT) technology and services are growing globally, especially around applications within the healthcare, energy, transport, public sector, and manufacturing industries. Many countries have led to the emergence of IoT/smart city projects.

Download Free demo of This Strategic Report: https://reportocean.com/industry-verticals/sample-request?report_id=HNY302014

The U.S. accounted for the major share in the global landscape in technology innovation. As per the World Economic Forum's 2018 Global Competitive Index, the country's competitive advantage is owing to its business vitality, substantial institutional pillars, financing agencies, and vibrant innovation ecosystem.

As of 2021, the U.S. region garnered 36%of the global information and communication technology (ICT) market share.Europe and China ranked as the second and third largest regions, separately accounting for 12%of the market share.The U.S. economy has held its global leadership position despite only a cumulative growth in wages from US$ 65 per hour in 2005 to US$ 71.3 per hour in 2015.

The prime objective of this report is to provide the insights on the post COVID-19 impact which will help market players in this field evaluate their business approaches. Also, this report covers market segmentation by major market verdors, types, applications/end users and geography(North America, East Asia, Europe, South Asia, Southeast Asia, Middle East, Africa, Oceania, South America)

By Types:

Natural Language Processing

Image Processing

By Applications:

IT

Law

Marketing

Customer Service

Airports and Ports

Bank

Telecom

Other

Key Indicators Analysed

Market Players & Competitor Analysis: The report covers the key players of the industry including Company Profile, Product Specifications, Production Capacity/Sales, Revenue, Price and Gross Margin 2016-2027 & Sales with a thorough analysis of the markets competitive landscape and detailed information on vendors and comprehensive details of factors that will challenge the growth of major market vendors.

Global and Regional Market Analysis: The report includes Global & Regional market status and outlook 2016-2027. Further the report provides break down details about each region & countries covered in the report. Identifying its sales, sales volume & revenue forecast. With detailed analysis by types and applications.

Market Trends:Market key trends which include Increased Competition and Continuous Innovations.

Opportunities and Drivers:Identifying the Growing Demands and New Technology

Porters Five Force Analysis: The report provides with the state of competition in industry depending on five basic forces: threat of new entrants, bargaining power of suppliers, bargaining power of buyers, threat of substitute products or services, and existing industry rivalry.

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Table of Content:

  • Market Definition and Overview
  • Research Method and Logic
  • Market Competition Analysis
  • Product and Service Analysis
  • Strategies for Company to Deal with the Impact of COVID-19
  • Market Segment by Type, Historical Data and Market Forecasts
  • Market Segment by Application, Historical Data and Market Forecasts
  • Market by by Region, Historical Data and Market Forecasts
  • Market Dynamic Analysis and Development Suggestions

Key Questions Answered in the Market Report

  • Which Manufacturing Technology is used for Market? What Developments Are Going on in That Technology?
  • Which Trends Are Causing These Developments? Who Are the Global Key Players in This Market?
  • What are Their Company Profile, Their Product Information, and Contact Information?
  • What Was Global Status of Market? What Was Capacity, Production Value, Cost and PROFIT of Market?
  • What Is Current Market Status of market Industry? What's Market Competition in This Industry, Both Company, and Country Wise?
  • What's Market Analysis of Market by Taking Applications and Types in Consideration?
  • What Are Projections of Global Market Industry Considering Capacity, Production and Production Value? What Will Be the Estimation of Cost and Profit?
  • What Will Be Market Share Report, Supply and Consumption? What about Import and Export?
  • What Is Market Chain Analysis by Upstream Raw Materials and Downstream Industry?

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COMTEX_411216555/2796/2022-07-29T01:48:28

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

Thu, 28 Jul 2022 17:48:00 -0500 en-US text/html https://www.marketwatch.com/press-release/cognitive-search-tools-market-2022-supply-demand-industry-research-and-end-user-analysis-forecasts-2030-2022-07-29
Killexams : Bot Services Market Growing at a CAGR 33.2% | Key Player Microsoft, IBM, Google, Oracle, AWS
Bot Services Market Growing at a CAGR 33.2% | Key Player Microsoft, IBM, Google, Oracle, AWS

“Microsoft (US), IBM (US), Google (US), Oracle (US), AWS (US), Meta (US), Artificial Solutions (Sweden), eGain (US), Baidu (China), Inbenta (US), Alvaria (US), SAP (Germany), Creative Virtual (UK), Gupshup (US), Rasa (US), Pandorabots (US), Botego (US), Chatfuel (US), Pypestream (US), Avaamo (US), Webio (Ireland), ServisBOT (US).”

Bot Services Market by Service Type (Platform & Framework), Mode of Channel (Social Media, Website), Interaction Type, Business Function (Sales & Marketing, IT, HR), Vertical (BFSI, Retail & eCommerce) and Region – Global Forecast to 2027

The Bot Services Market size to grow from USD 1.6 billion in 2022 to USD 6.7 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 33.2% during the forecast period. Various factors such as rise in the need for 24X7 customer support at a lower operational cost, integration of chatbots with social media to augment marketing strategy, and innovations in AI and ML technologies for chatbots resulting in better customer experience are expected to drive the adoption of bot services.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=54449873

According to Microsoft, Azure Bot Service provides an integrated development environment for bot building. Its integration with Power Virtual Agents, a fully hosted low-code platform, enables developers of all technical abilities to build conversational AI bots without the need for any further coding. The integration of Azure Bot Service and Power Virtual Agents enables a multidisciplinary team with a range of expertise and abilities to build bots inside a single software as a service (SaaS) solution.

Healthcare and Life Sciences vertical to witness the highest CAGR during the forecast period

The segmentation of the bot services market by vertical includes BFSI, retail & eCommerce, healthcare & life sciences, media & entertainment, travel & hospitality, IT & telecom, government, and others (automotive, utilities, education and real estate). The healthcare industry is developing rapidly due to many major technological advancements to enhance the overall patients experience. Hospitals and other health institutions are increasingly adopting bot services to Strengthen the overall experience of patients, doctors, and other staff. Additionally, bot services can enhance patient experience and build patient loyalty, while improving organizational efficiency. Moreover, bots, also known as virtual health assistants, notify patients about their medication plan, address concerns, deliver diagnosis reports, educate them regarding certain diseases, motivate them to exercise, and personalize user experience.

Some major players in the bot services market include Microsoft (US), IBM (US), Google (US), Oracle (US), AWS (US), Meta (US), Artificial Solutions (Sweden), eGain (US), Baidu (China), Inbenta (US), Alvaria (US), SAP (Germany), CM.com (Netherlands), Creative Virtual (UK), Kore.ai (US), [24]7.ai (US), Gupshup (US), Rasa (US), Pandorabots (US), Botego (US), Chatfuel (US), Pypestream (US), Avaamo (US), Webio (Ireland), ServisBOT (US), Morph.ai (India), Cognigy (Germany), Enterprise Bot (Switzerland), Engati (US), and Haptik (US). These players have adopted various organic and inorganic growth strategies, such as new product launches, partnerships and collaborations, and mergers and acquisitions, to expand their presence in the global bot services market.

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Artificial Solutions (Sweden) is a leading specialist in Conversational AI solutions and services. The solution offered by the company enables communication with applications, websites, and devices in everyday, human-like natural language via voice, text, touch, or gesture inputs. Artificial Solutions’ conversational AI technology makes it easy to build, implement, and manage a wide range of natural language applications, such as virtual assistants, conversational bots, and speech-based conversational UIs for smart devices. Artificial Solutions offers bot services and solutions to various industries, such as financial services, retail, automotive, telecom, energy and utilities, travel and leisure, and entertainment. Artificial Solutions has won several awards, such as the 2019 Stevie Awards for Sales and Customer Service, the 2018 Speech Industry Awards, and the 2018 AICONICS: Best Intelligent Assistant Innovation. The company’s major customers include AT&T, Shell, Vodafone, TIAA, Volkswagen Group, Deutsche Post, Widiba, Telenor Group, Accenture, KPMG, Cognizant, Wipro, and Publicis Sapient. It has development centers in Barcelona, Hamburg, London, and Stockholm and offices across Europe, Asia Pacific, and South America.

In the bot services market, it provides Teneo, a platform that enables business users and developers to collaborate to create intelligent conversational AI applications. These applications operate across 35 languages, multiple platforms, and channels in record time.

eGain (US) is a leading cloud customer engagement hub software supplier. eGain products have been used to Strengthen customer experience, streamline service processes, and increase revenue across the online, social media, and phone channels for over a decade. eGain helps hundreds of the worlds leading organizations turn their disjointed sales and customer service operations into unified customer engagement hubs (CEHs). In North America, Europe, the Middle East, Africa, and Asia Pacific, eGain Corporation develops, licenses, implements, and supports customer service infrastructure software solutions. It offers a unified cloud software platform to automate, augment, and orchestrate consumer interactions. It also provides subscription services, which deliver users access to its software via a cloud-based platform, as well as professional services, including consultation, implementation, and training. The company caters to the financial services, telecommunications, retail, government, healthcare, and utilities industries.

In the bot services market, the company offers AI Chatbot Virtual Assistant software which improves customer engagement. The VA acts as a guide, helping customers navigate the website and taking them to the relevant places on a page. The virtual assistant provides answers to any queries, even helping in making shopping decisions.

Baidu (China) provides internet search services. It is divided into two segments: Baidu Core and iQIYI. The Baidu app helps customers to access search, feed, and other services through their mobile devices. Baidu Search helps users to access the companys search and other services. Baidu Feed gives users a customized timeline based on their demographics and interests. The company provides products, including Baidu Knows, an online community where users can ask questions to other users; Baidu Wiki; Baidu Healthcare Wiki; Baidu Wenku; Baidu Scholar; Baidu Experience; Baidu Post; Baidu Maps, a voice-enabled mobile app that provides travel-related services; Baidu Drive; Baijiahao; and DuerOS, a smart assistant platform. The company also provides online marketing services such as pay for performance, an auction-based service that enables customers to bid for priority placement of paid sponsored links and reach users searching for information about their products or services. Other marketing services offered by the company are display-based marketing services and other online marketing services based on performance criteria other than cost per click. The company offers a mobile ecosystem, which includes Baidu A, a portfolio of applications. Further, the company provides iQIYI, an online entertainment service, including original and licensed content; video content and membership; and online advertising services.

In the bot services market, Baidu offers Baidu Bot, a search bot software used by Baidu, which collects documents from the web to build a searchable index for the Baidu search engine.

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"Adobe (US), IBM (US), Google (US), Oracle (US), Salesforce (US), Sprout Social (US), Hootsuite (Canada), Meltwater (US), Sprinklr (US), Digimind (France), HubSpot (US), Clarabridge (US), Khoros (US), Falcon.io (Denmark), Zoho Corporation (India), NetBase (US), Brandwatch (UK), Talkwalker (Europe), Buffer (US), Agorapulse (France), Sendible (UK), MavSocial (US)."

Social Media Management Market by Component (Solutions (Social Media Marketing, Social Media Asset and Content Management), Services), Deployment Mode, Organization Size, Application, (Sales and Marketing), Vertical - Global Forecast to 2026

The Social Media Management Market size is projected to grow from USD 14.4 billion in 2021 to USD 41.6 billion in 2026, at a Compound Annual Growth Rate (CAGR) of 23.6% during the forecast period. The major factors driving the growth of the Social Media Management market include the rise in need in focus on the social media management market and competitive intelligence, need to drive search RoI for social media strategy, enhancement of customer experience with social media management, the shift of people toward virtual realm due to COVID-19 and the increase in user engagement of social media using smartphones.

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Based on solutions, risk and compliance management solutions segment to grow at a higher CAGR during the forecast period

Social media has become a powerful tool for businesses to use in connecting with their customers. The medium has certainly made major corporations more accessible to the consumer. But with this ease of accessibility comes risk in many forms. Risk and compliance management incorporates platforms and solutions to maintain the integrity and security of content published on social media and sensitive information, including customer data and company contact.

Based on deployment mode, on-premises expected to hold a small part of market size as compare to cloud during the forecast period

On-premises social media management solutions are hosted in the client environment, where the social media management solution is hosted by customers on their own IT infrastructure. In this deployment mode, the customer side manages the supporting IT infrastructure, security patches, maintenance, updates, and monitors physical or virtual servers for data storage. Social media management vendors or their partners work together with the client to install and configure the solution in the client’s IT environment. On-premises social media management is offered through license/annual subscription where a client pays a fixed annual fee, including maintenance.

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Major Social Media Management vendors include Adobe (US), IBM (US), Google (US), Oracle (US), Salesforce (US), Sprout Social (US), Hootsuite (Canada), Meltwater (US), Sprinklr (US), Digimind (France), HubSpot (US), Clarabridge (US), Khoros (US), Falcon.io (Denmark), Zoho Corporation (India), NetBase (US), Brandwatch (UK), Talkwalker (Europe), Buffer (US), Agorapulse (France), Sendible (UK), MavSocial (US), Socialbakers (Czech Republic), Synthesio (US), and eClincher (US). These market players have adopted various growth strategies, such as partnerships, collaborations, and new product launches, to expand have been the most adopted strategies by major players from 2019 to 2021, which helped companies innovate their offerings and broaden their customer base.

Adobe (US), is one of the leading vendors in the Social Media Management market. The company is one of the prominent players focusing on developing multimedia and creativity software products and services. Since its inception, the company has been offering social media management solutions. It partnered with social media management platforms to support brands delivering personalized and relevant experiences across the entire customer journey. The company offers Adobe Experience Cloud products. It is a collection of applications and services. It comes with various functionalities which help gather insights for audience, content management, campaign management, customer engagement, advertisement management, channel management, and more. It allows measuring the success of campaigns, which helps predict and understand customer behavior. Adobe integrates with ML to optimize and test web and mobile applications.

IBM (US) is another provider of Social Media Management solutions and services across the globe. It caters to the latest technologies and services to its clientele across the globe. The company offers a vast portfolio of social media marketing and social media management products and solutions for helping its clients in enhancing their decision-making. It is constantly upgrading its capabilities for assisting its customers in improving their brands with the help of intelligent media marketing and media management solutions. It integrates technologies, such as AI and blockchain, to expand social media management and cloud platform. For instance, in 2021, IBM acquired myInvenio to enhance the hybrid-cloud and AI capabilities of the company and further enable its clients to optimize business processes. In 2018, IBM acquired IRIS.TV to increase viewer engagement with the help of a new solution, Video Recommendations. IBM is a reputed IT company and has a very strong and widely scattered customer base across the globe.

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