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IBM Sterling Order Management V9.2 Solution Design
IBM Management learning
Killexams : IBM Management learning - BingNews https://killexams.com/pass4sure/exam-detail/C8010-241 Search results Killexams : IBM Management learning - BingNews https://killexams.com/pass4sure/exam-detail/C8010-241 https://killexams.com/exam_list/IBM 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 : IILM University partners with IBM to provide students skill training on new-age technologies No result found, try new keyword!The program will help students gain competitive edge over others during interviews, internships along with IBM's globally-recognised Digital Badge, in addition to the degree offered by the University. Mon, 08 Aug 2022 19:19:21 -0500 en-in text/html https://www.msn.com/en-in/money/news/iilm-university-partners-with-ibm-to-provide-students-skill-training-on-new-age-technologies/ar-AA10sASO?fromMaestro=true Killexams : Learning Management System Market to See Huge Growth by 2027 : Xerox, IBM, SAP

The latest study released on the Global Learning Management System Market by AMA Research evaluates market size, trend, and forecast to 2027. The Learning Management System market study covers significant research data and proofs to be a handy resource document for managers, analysts, industry experts and other key people to have ready-to-access and self-analyzed study to help understand market trends, growth drivers, opportunities and upcoming challenges and about the competitors.

Key Players in This Report Include:

Cornerstone Ondemand, Inc. (United States), Xerox Corporation (United States), IBM Corporation (United States), Net dimensions Ltd. (United States), SAP Se (Germany), Blackboard, Inc. (United States), Saba Software, Inc. (United States), Mcgraw-Hill Companies (United States), Pearson Plc (United Kingdom), D2L Corporation (Canada),

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Definition:

Learning management system basically a software application for the administration, documentation, tracking, reporting, and delivery of educational courses, training programs, and many others. The learning management system highly emerges from e-Learning.

Market Trends:

Increasing Competition Among Market Players

High Adoption of Cloud-Based Solutions

Market Drivers:

Growing Awareness Towards the Adoption of Digital Learning

Rapid Inclination to BYOD Policy and Enterprise Mobility

Widespread of Government Initiatives for Growth Of LMS

Growing Implication Of E-Learning in Corporates

Market Opportunities:

Growing Demand for Gamification in LMS Delivers Strong Opportunities for LMS Providers

High Surge in Demand for Collaborative Learning in LMS to Provide High Potentials for Trainees

The Global Learning Management System Market segments and Market Data Break Down are illuminated below:

by Type (Academic, Corporate), Industry Verticals (Banking, Financial Services, and Insurance, Healthcare, Retail, Government, Manufacturing, Others), Delivery Mode (Distance Learning, Instructor-Led Training), Organizations Size (Small and Medium Size Enterprises, Large Size Enterprises), Offerings (Solution, Services)

Global Learning Management System market report highlights information regarding the current and future industry trends, growth patterns, as well as it offers business strategies to helps the stakeholders in making sound decisions that may help to ensure the profit trajectory over the forecast years.

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Geographically, the detailed analysis of consumption, revenue, market share, and growth rate of the following regions:

  • The Middle East and Africa (South Africa, Saudi Arabia, UAE, Israel, Egypt, etc.)
  • North America (United States, Mexico & Canada)
  • South America (Brazil, Venezuela, Argentina, Ecuador, Peru, Colombia, etc.)
  • Europe (Turkey, Spain, Turkey, Netherlands Denmark, Belgium, Switzerland, Germany, Russia UK, Italy, France, etc.)
  • Asia-Pacific (Taiwan, Hong Kong, Singapore, Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia).

Objectives of the Report

  • -To carefully analyze and forecast the size of the Learning Management System market by value and volume.
  • -To estimate the market shares of major segments of the Learning Management System
  • -To showcase the development of the Learning Management System market in different parts of the world.
  • -To analyze and study micro-markets in terms of their contributions to the Learning Management System market, their prospects, and individual growth trends.
  • -To offer precise and useful details about factors affecting the growth of the Learning Management System
  • -To provide a meticulous assessment of crucial business strategies used by leading companies operating in the Learning Management System market, which include research and development, collaborations, agreements, partnerships, acquisitions, mergers, new developments, and product launches.

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Major highlights from Table of Contents:

Learning Management System Market Study Coverage:

  • It includes major manufacturers, emerging player’s growth story, and major business segments of Learning Management System market, years considered, and research objectives. Additionally, segmentation on the basis of the type of product, application, and technology.
  • Learning Management System Market Executive Summary: It gives a summary of overall studies, growth rate, available market, competitive landscape, market drivers, trends, and issues, and macroscopic indicators.
  • Learning Management System Market Production by Region Learning Management System Market Profile of Manufacturers-players are studied on the basis of SWOT, their products, production, value, financials, and other vital factors.
  • Key Points Covered in Learning Management System Market Report:
  • Learning Management System Overview, Definition and Classification Market drivers and barriers
  • Learning Management System Market Competition by Manufacturers
  • Impact Analysis of COVID-19 on Learning Management System Market
  • Learning Management System Capacity, Production, Revenue (Value) by Region (2021-2027)
  • Learning Management System Supply (Production), Consumption, Export, Import by Region (2021-2027)
  • Learning Management System Production, Revenue (Value), Price Trend by Type {Academic, Corporate,}
  • Learning Management System Manufacturers Profiles/Analysis Learning Management System  Manufacturing Cost Analysis, Industrial/Supply Chain Analysis, Sourcing Strategy and Downstream Buyers, Marketing
  • Strategy by Key Manufacturers/Players, Connected Distributors/Traders Standardization, Regulatory and collaborative initiatives, Industry road map and value chain Market Effect Factors Analysis.

Browse Complete Summary and Table of Content @ https://www.advancemarketanalytics.com/reports/4918-global-learning-management-system-market

Key questions answered

  • How feasible is Learning Management System market for long-term investment?
  • What are influencing factors driving the demand for Learning Management System near future?
  • What is the impact analysis of various factors in the Global Learning Management System market growth?
  • What are the exact trends in the regional market and how successful they are?

Thanks for studying this article; you can also get individual chapter wise section or region wise report version like North America, Middle East, Africa, Europe or LATAM, Southeast Asia.

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Sun, 17 Jul 2022 20:09:00 -0500 Newsmantraa en-US text/html https://www.digitaljournal.com/pr/learning-management-system-market-to-see-huge-growth-by-2027-xerox-ibm-sap
Killexams : IILM University signs MoU with IBM, illuminating students about new-age technologies and the exclusive IBM Digital Badge

Greater Noida : IILM University, Greater Noida, signed a Memorandum of Understanding (MoU) with IBM Innovation Centre for Education in August , 2022. The MoU was signed by Vice-Chancellor, IILM University, Dr. Taruna Gautam and Program Director, IBM Innovation Centre for Education, Mr. Vithal Madyalkar. Among those who attended the formal signing ceremony included Mr. R. Hari, IBM leader for Business Development & Academia relationships, Dr. Raveendranath Nayak, Director-IILM Graduate School of Management and Dr. Shilpy Agrawal, Head of Computer Science and Engineering Department, IILM University, Greater Noida.

Commenting over the collaboration between the two knowledge hubs, Dr. Taruna Gautam, Vice-Chancellor IILM University, Greater Noida, IILM University, said, “We are extremely excited about the new development as it aligns with our core aim to raise a race of competent professionals and make them future-ready. As part of the newly formed alliance, IBM would offer the university students much-needed applied IT knowledge, establishing a structured learning pathway. IBM’s Innovation Centre for Education Programs would impart students with information about the emerging technologies and in-demand industry domains like Cloud Computing and Virtualization, Data Sciences & Business Analytics, Graphics and Gaming Technology, Artificial Intelligence, Machine Learning, Blockchain, Cyber Security and Forensics, IT Infrastructure management, and Internet of Things.”

The students will also get a chance to enhance their skills pertaining to information technology required for operating different business domains such as Telecom informatics, Banking, Financial services and Insurance informatics, e-commerce & Retail Informatics, and Healthcare Informatics.

IBM Innovation Centre for Education offers various unique, time-tested initiatives and skills developed by IBM Trained & Certified faculty & Technology Experts. The in-depth and applied courseware powered by IBM will be exclusively available to the students at IILM University. The new progression is in line with the NEP 2020 norms, promoting the project and lab-based learning combined with Instructor-led classroom training.

The program will help students gain not only a competitive edge over others during interviews, internships as well as national and International contests, but also IBM’s globally-recognized Digital Badge, in addition to the degree offered by the University.

Mon, 08 Aug 2022 06:06:00 -0500 en-US text/html https://indiaeducationdiary.in/iilm-university-signs-mou-with-ibm-illuminating-students-about-new-age-technologies-and-the-exclusive-ibm-digital-badge/
Killexams : How A New Kind Of Business Model Creates Digital Winners

Back in the “olden days” of 2005, it was obvious that a firm needed to choose between two types of business architecture, as business guru Geoffrey Moore explained in his famous 2005 article in Harvard Business Review, “Strategy and Your Stronger Hand.” “There were really only two organizational [business models] to choose between,” Moore wrote. You either had high-value complex, interactive operations with a small number of customers, or high-volume operations with a large number of customers, each paying very little. (Figure 1) Firms had to choose one model or the other, using their “stronger hand.” Even today, Moore urges firms to choose between the two. Ambidexterity is “not only very rare.” Moore declares: “There is no third model that scales.”

Yet during the 2010s, something strange was happening. Even as Moore was writing, a third business model that scaled was in gestation. Firms were beginning to use digital technology to achieve organizational ambidexterity at scale. Such firms could generate complex interactive operations with very large numbers of customers. (Figure 2) In due course, firms with this ambidexterity became the digital winners of the new era—Amazon, Apple, Facebook Google, Microsoft, and Tesla. The result was huge profitability. (Figure 3)

Three Different Business Architectures

To understand the interactive volume model of the digital winners, let’s start by comparing it to the older models of “complex operations” and “volume operations.”

1. Complex operations

Complex operations, such as IBM, Cisco, SAP, and McKinsey, catered to small numbers of wealthy customers with difficult problems. They provided unique solutions for each customer situation. They worked with customers who were willing to pay a high price for personally interactive service from experts. Value was created by interacting with customers, understanding their needs, and developing solutions to meet those needs. Vendors had customers in the thousands, not millions, with a small number of transactions per customer per year, for a very high price.

2. Volume operations

By contrast, volume operations focused on delivering standardized solutions for large numbers of customers for a relatively low price. Firms addressed generic customer situations—simple repetitive jobs being done for all customers. Think Nestlé, Procter & Gamble, and Kellogg. Customers got a standardized product. Value lay in meeting a common need. Vendors sought millions of customers with tens or even hundreds of transactions per customer per year, typically for a few dollars per transaction. Interaction with individual customers were costly and to be avoided. The firm crafted its products based mainly on quantitative surveys and market tests.

In 2005, these two models constituted a comprehensive picture of the business landscape at the time. There was only a small overlap between the two models (Figure 1). Firms, said Moore, needed to understand what kind of business model their firm was in and stick to that. That was their “dominant hand.” That determined the kind of people and practices that flourished in the firm. Firms needed to avoid the temptation of trying to succeed in the opposite type of business model.

3. Interactive Volume Operations

Then in 2010s. a third type of business model emerged: interactive volume operations. Some firms began providing interactive solutions for millions of customers for a very low price, or even free. Think Facebook or Google providing myriad solutions at no direct cost to the customer, while monetizing their knowledge of customers through advertising. Or Amazon having a unique interactive relationship with millions of customers, about whose wishes and interests Amazon knew a lot, and could use that knowledge to offer new products in a friction-less manner. The business model often enjoyed massive network effects.

As with complex operations, such firms had an interactive relationship with each individual customer, but now through digital technology. Indeed, value for customers was co-created through instant, friction-less digital interactions. The firm was able to achieve high-quality interactions through a combination of digital technology and customer-driven mindsets. Empathy became a key value. Learning about the customer came mainly from interactions with customers, along with high-powered computing, using the Cloud and artificial intelligence.

The interactivity thus came, not from interacting with human beings, but rather by interacting with the product or service itself. (Figure 4)

Although the interactive volume business model could operate with huge numbers of customers, its mode of operation was very different from the traditional volume model, which was based on generic, standardized inert products, where the focus was on eliminating any variation. Learning about the customers was carried out by quantitative research and market testing. (Figure 5)

A Key Feature Of The Interactive Volume Model: A Different Mindset

The huge financial gains made by the digital winners tempted traditional firms to emulate their success with “digital transformations,” that involved large investments in technology and IT staff. Yet the initiatives were generally disappointing. That was because the new way of operating wasn’t just a matter of technology. It involved a radically different management mindset.

Thus, both the older volume operations and complex operations models had a traditional business mindset. The goal of the firm was to make money for the company and its shareholders. And the structure of the firm was a vertical hierarchy of authority, with units operating with silos. (Figure 6)

That was quite different from the mindset required by the complex interactive model. Here the goal of the firm shifted to co-creating value for customers; long-term shareholder value and profits were seen as results of business models, not goals. Work was typically conducted in self-organizing, agile teams, not individuals reporting to bosses. The firm’s structure shifted from a vertical hierarchy of authority to a horizontal, interactive network of competence.

Figure 6

The Role Of The Three Models

All three models play a role in today’s economy.

1. Complex operations

Columbia University management professor Rita McGrath points to Steve Blank’s book “4 Steps to the Epiphany,” and his blog: in the ideal complex systems, when customers had a problem, they tended to be “aware that they had a problem, had been actively looking for a solution, had put together a not-so-great solution, and had or could acquire a budget.” Credibility was key. “For their part,” writes McGrath, “complex systems buyers want to know why they should trust you to solve their problems versus available alternatives.”

2. Volume operations

The goal in volume operations was to eliminate any barriers to consumption by users and encourage repeat purchasing and word-of-mouth referrals. This meant removing any variation or customization. In this model, designers and engineers were unlikely to share traits with the millions of customers. Market research was quantitative and experimental market tests. Significant parts of the economy are still operating in the volume operations model.

3. Interactive volume model

Nevertheless, the interactive volume model can be an unexpected threat to the volume customer model. Even the famed management theorist Clayton Christensen said at the time of the release of the iPhone in 2007 that it “wasn’t truly disruptive.” It was “a product that the existing players in the industry are heavily motivated to beat,” and that “its probability of success is going to be limited.” Five years later, in 2012, Christensen was still saying that the iPhone would soon succumb to price competition and modular knockoffs. “History,” Christensen said, “speaks pretty loudly on that.”

Ten years after that, in 2022, and trillions of dollars more in profits, the iPhone was still going strong. What Christensen missed was that Apple had created not just a volume operations product, but an interactive digital product. The iPhone wasn’t just a phone. It was a constantly evolving, interactive, multi-function device that devastated many other products and services, including address books, video cameras, pagers, wristwatches, maps, books, travel games, flashlights, dictation recorders, music players, and many more.

Christensen’s “loud lessons” from the history, in which one firm’s products compete against another’s, didn’t apply to firms that are innovating in the digitally interactive mode. An entirely new game was being played. In this new game, innovation could transform many other products and disrupt the dynamic of the volume operations model.

Thus, even a management theorist as brilliant as Christensen allowed belief in his own theory of management models to shield him from what was happening in front of his eyes.

And read also:

Why Digital Transformations Are Failing

How Empathy Helped Generate A $2 Trillion Company

Tue, 09 Aug 2022 09:09:00 -0500 Steve Denning en text/html https://www.forbes.com/sites/stevedenning/2022/08/09/how-a-new-kind-of-business-model-created-digital-winners/
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 : A wealth of learning

She is rich in not just wealth, but in the diversity of her interests and experiences that straddle spearheading an IT business, doing philanthropy, producing films and wildlife conservation.

To start with, Roshni Nadar Malhotra, the 40-year old chairperson of India’s third largest software firm HCL Technologies, and Trustee, Shiv Nadar Foundation whose net worth is ₹84,330 crore, according to Hurun-Kotak Private Bank’s Leading Wealthy Women report, wanted a career in media. Accordingly, the heiress to the IT services firm that Shiv Nadar commanded, studied radio, television and film, interning with CNN and working with in Sky News in London.

But her dad nudged her into business studies – she did her MBA in Social Enterprise Management from Kellogg, before throwing her into the deep end as CEO of HCL Corporation, the holding company of HCL Tech, in 2009, when she was just 28. Nadar Malhotra, however, learnt the ropes pretty fast and earned a board seat by 2013. In 2019, HCL announced, it would acquire selected select IBM products in a deal worth $1.8 billion, which pivoted the company from IT and solutions to embrace products and platforms. The challenge before Nadar Malhotra, who took over from her dad in 2020, is to ensure the scaling of those streams. A product portfolio transformation is currently underway at HCL, which like other IT Services firms grew on the back of demand for cloud transformation and application during the pandemic. However, the products and platforms business did not do so well.

Significantly, if Nadar Malhotra is the wealthiest businesswoman, HCL Technologies CEO and MD C Vijaykumar is the highest paid IT sector CEO with compensation of $16.52 million during the 2021-2022 financial year.

A major part of Nadar Malhotra’s time is devoted to the Shiv Nadar Foundation, under which the Shiv Nadar University, the Shiv Nadar Schools, VidyaGyan, Kiran Nadar Museum of Art) and some other initiatives – are run. Delivering quality education is a dream for the Nadars.

From her mother Kiran, she has inherited an eye for art. Given the gamut of her interests, there’s a wealth of opportunities that life holds for this multifaceted lady.

Published on July 31, 2022

Sun, 31 Jul 2022 03:33:00 -0500 en text/html https://www.thehindubusinessline.com/specials/corporate-file/a-wealth-of-learning/article65706939.ece
Killexams : Learning with the Internet of Things and Artificial Intelligence: harnessing their potential No result found, try new keyword!According to the e-learning industry, over 47% of learning management tools will incorporate artificial intelligence within the next three years. Fri, 29 Jul 2022 18:19:58 -0500 en-in text/html https://www.msn.com/en-in/news/other/learning-with-the-internet-of-things-and-artificial-intelligence-harnessing-their-potential/ar-AA107Gmq Killexams : IBM extends Power10 server lineup for enterprise use cases

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


IBM is looking to grow its enterprise server business with the expansion of its Power10 portfolio announced today.

IBM Power is a RISC (reduced instruction set computer) based chip architecture that is competitive with other chip architectures including x86 from Intel and AMD. IBM’s Power hardware has been used for decades for running IBM’s AIX Unix operating system, as well as the IBM i operating system that was once known as the AS/400. In more exact years, Power has increasingly been used for Linux and specifically in support of Red Hat and its OpenShift Kubernetes platform that enables organizations to run containers and microservices.

The IBM Power10 processor was announced in August 2020, with the first server platform, the E1080 server, coming a year later in September 2021. Now IBM is expanding its Power10 lineup with four new systems, including the Power S1014, S1024, S1022 and E1050, which are being positioned by IBM to help solve enterprise use cases, including the growing need for machine learning (ML) and artificial intelligence (AI).

What runs on IBM Power servers?

Usage of IBM’s Power servers could well be shifting into territory that Intel today still dominates.

Steve Sibley, vp, IBM Power product management, told VentureBeat that approximately 60% of Power workloads are currently running AIX Unix. The IBM i operating system is on approximately 20% of workloads. Linux makes up the remaining 20% and is on a growth trajectory.

IBM owns Red Hat, which has its namesake Linux operating system supported on Power, alongside the OpenShift platform. Sibley noted that IBM has optimized its new Power10 system for Red Hat OpenShift.

“We’ve been able to demonstrate that you can deploy OpenShift on Power at less than half the cost of an Intel stack with OpenShift because of IBM’s container density and throughput that we have within the system,” Sibley said.

A look inside IBM’s four new Power servers

Across the new servers, the ability to access more memory at greater speed than previous generations of Power servers is a key feature. The improved memory is enabled by support of the Open Memory Interface (OMI) specification that IBM helped to develop, and is part of the OpenCAPI Consortium.

“We have Open Memory Interface technology that provides increased bandwidth but also reliability for memory,” Sibley said. “Memory is one of the common areas of failure in a system, particularly when you have lots of it.”

The new servers announced by IBM all use technology from the open-source OpenBMC project that IBM helps to lead. OpenBMC provides secure code for managing the baseboard of the server in an optimized approach for scalability and performance.

E1050

Among the new servers announced today by IBM is the E1050, which is a 4RU (4 rack unit) sized server, with 4 CPU sockets, that can scale up to 16TB of memory, helping to serve large data- and memory-intensive workloads.

S1014 and S1024

The S1014 and the S1024 are also both 4RU systems, with the S1014 providing a single CPU socket and the S1024 integrating a dual-socket design. The S1014 can scale up to 2TB of memory, while the S1024 supports up to 8TB.

S1022

Rounding out the new services is the S1022, which is a 1RU server that IBM is positioning as an ideal platform for OpenShift container-based workloads.

Bringing more Power to AI and ML

AI and ML workloads are a particularly good use case for all the Power10 systems, thanks to optimizations that IBM has built into the chip architecture.

Sibley explained that all Power10 chips benefit from IBM’s Matrix Match Acceleration (MMA) capability. The enterprise use cases that Power10-based servers can help to support include organizations that are looking to build out risk analytics, fraud detection and supply chain forecasting AI models, among others. 

IBM’s Power10 systems support and have been optimized for multiple popular open-source machine learning frameworks including PyTorch and TensorFlow.

“The way we see AI emerging is that a vast majority of AI in the future will be done on the CPU from an inference standpoint,” Sibley said.

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.

Mon, 11 Jul 2022 09:01:00 -0500 Sean Michael Kerner en-US text/html https://venturebeat.com/programming-development/ibm-extends-power10-server-lineup-for-enterprise-use-cases/
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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https://twitter.com/cobaltiron
https://www.linkedin.com/company/cobalt-iron/
https://www.youtube.com/user/CobaltIronLLC

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