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Exam Code: C9550-413 Practice exam 2022 by Killexams.com team
C9550-413 IBM Operational Decision Manager Advanced V8.7 Application Development

Exam Title : IBM Certified Application Developer - Operational Decision Manager Advanced V8.7
Exam ID : C9550-413
Exam Duration : 150 mins
Questions in exam : 68
Passing Score : 64%
Official Training : Developing Rule Solutions in IBM Operational Decision Manager V8.7
Exam Center : Pearson VUE
Real Questions : IBM Operational Decision Manager Advanced Application Development Real Questions
VCE VCE exam : IBM C9550-413 Certification VCE Practice Test

Business Rules Applications: Rule Analysis, Design and Development
- Analyze rule requirements.
- Design and create classic and decision service rule projects.
- Design and implement an Execution Object Model (XOM) and Business Object Model (BOM).
- Design and construct variables (i.e., rule variable, ruleset variable and ruleset parameter).
- Organize rule packages and develop rule flows.
- Author rule artifacts (e.g., action rules, decision tables, decision trees, action rule templates and decision table templates). 16%
Business Rules Applications: Rule Validation
- Interpret analysis of rule artifacts for consistency and completeness.
- Perform rule artifact testing with DVS scenarios.
- Configure the testing functionality in the Business console.
- Configure simulation functionality in the Business console for metrics, key performance indicators (KPIs), data, report formats, simulations.
- Troubleshoot and debug rule execution logic. 12%
Business Rules Applications: Rule Deployment and Governance
- Configure deployment configurations and RuleApp definitions.
- Configure the deployment of a Managed Java XOM.
- Configure role-based permissions in the Enterprise console.
- Merge branches in Enterprise Console.
- Synchronize rules with Rule Designer, Decision Center, Rule Solutions for Office (RSO), and Source Code Control (SCC).
- Manage and monitor RuleApps in the Rule Execution Server console.
- Automate the build and deployment of RuleApps and Managed Java XOMs. 12%
Business Rules Applications: Rule Execution and Application Integration
- Identify and explain the appropriate rule execution invocation options (e.g., rule session API client applications, transparent decision services, REST APIs).
- Develop rule session API client applications (e.g., standalone Java SE, RES Java SE, RES Java EE).
- Configure the Decision Warehouse monitoring and filter options.
- Troubleshoot and debug the Rule Execution Server environment. 9%
Business Rules Applications: Rule Application Performance Tuning
- Select appropriate rule execution mode (e.g., algorithm, exit criteria).
- Tune Rule Execution Server performance.
- Tune Decision Center performance.
- Explain the capabilities of decision engine and classic engine. 7%
Business Rules Applications: Customization of Business User Experience
- Construct rule authoring customizations (e.g., dynamic domains, value editors).
- Implement a rule model extension.
- Create custom DVS scenario providers.
- Perform customization of Decision Center GUI.
- Perform customization of Decision Center rule life-cycle.
- Construct custom rule reports.
- Explain Business Rules Embedded. 12%
Insights Applications: Solution Design and Development
- Create solution projects.
- Define a business model and import events and entities.
- Define aggregates.
- Design and configure rule, Java and predictive scoring agent projects.
- Author action rules in rule agents.
- Develop Java artifacts in Java agents.
- Develop predictive scoring agent classes.
- Develop solution extensions (i.e., data providers, entity initialization extensions).
- Analyze the significance of modifying the event time horizon. 18%
Insights Applications: Solution Deployment and Testing
- Manage solution versioning and packaging.
- Export and deploy solutions.
- Create test clients and use REST and Gateway APIs.
- Record events and inspect events with Insight Inspector. 6%
Insights Applications: Solution Connectivity and Application Integration
- Design and create the inbound connectivity for a solution.
- Design and create the outbound connectivity for a solution.
- Export and deploy connectivity solutions. 4%
Insights Applications: Solution Performance, Tuning and Availability
- Tune Insight Server performance.
- Troubleshoot and monitor Insight Server.
- Restore data after system shut down. 4%

IBM Operational Decision Manager Advanced V8.7 Application Development
IBM Operational certification
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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 Improve 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 Improve 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 Improve 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 : Cybrary confronts the cyberskills gap head on; raises $25M

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As we move deeper into 2022, almost every company is feeling the cyberskills gap to some degree. Now with the cyber workforce gap hitting 2.72 million, it’s unsurprising that IBM research recently found that 83% of organizations have had more than one data breach.  

With the workforce gap showing no sign of closing, training is becoming critical for employees to teach cybersecurity professionals the skills they need to thrive amid today’s complex threat landscape. 

Addressing the cyberskills gap 

As the cyberskills gap continues to grow, more and more organizations are recognising the need to use training — rather than hiring — to fix the shortage. 

“Studies continue to show that a cybersecurity staffing shortage is placing organizations at risk, and the skills shortage and its associated impacts have not improved over the past few years,” said Kevin Hanes, CEO of Cybrary, a cybersecurity skills training platform. 

“Products and technology will not help solve this fundamental issue; rather, investing in people is key to narrowing the cybersecurity skills gap and helping to combat increasing burnout and human error,” Hanes said. 

Hanes says that Cybrary is aiming to address these challenges by providing cybersecurity practitioners with the “right training at the right time” to equip them to respond to modern threats. 

It does this by providing them with a platform they can use to access learning materials and prepare for professional certifications with scenario-based training and over 1,900 learning activities. 

A look at the IT training market 

Cybrary is competing against a range of cybersecurity training providers that offer online, in-person training and boot camps. The provider sits loosely within the global IT training market, which researchers valued at $68 billion in 2020, and estimate will reach a value of $97.6 billion by 2026. 

One of Cybrary’s competitors is Pluralsight, which offers a mixture of courses, skill-assessments labs, and hands-on learning developed by industry experts on syllabus such as Microsoft Azure Deployment, AWS Operations and Ruby Language Fundamentals. 

Pluralsight most recently announced raising $430.4 million in revenue in 2020. 

Another competitor is Infosec, a cybersecurity training and security awareness training provider with over 2,000 resources, including over 1,400 cybersecurity courses and cyber ranges, and live boot camps with instructor-led training. According to Zoominfo, Infosec has raised $31 million in revenue

However, Hanes argues that Cybrary differentiates itself from other solutions on the market by offering up-to-date learning material at a lower price point. 

“Cybrary’s platform allows individuals and teams to skill up on their own time from anywhere in the world. And with the Cybrary Threat Intelligence Group (CTIG) and SMEs developing new content in real time, Cybrary users can be confident that we are providing them with high-quality training that covers the latest threats and vulnerabilities impacting the industry.” 

Today, Cybrary announced it has raised $25 million as part of a series C funding round, bringing its total funding to $48 million following a $19 billion series B funding round in 2019. 

The organization intends to use the funding to enhance its R&D across engineering, product and marketing teams, while growing the capabilities of the Cybrary Threat Intelligence Group.

More broadly, the funding highlights that investors are looking to security training as a potential solution to bridge the cyberskills gap. 

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Tue, 02 Aug 2022 08:00:00 -0500 Tim Keary en-US text/html https://venturebeat.com/security/cybrary-cyber-skills-gap/
Killexams : Smart Port Market Worth $5.7 Billion By 2027 - Exclusive Report By Marketsandmarkets™' No result found, try new keyword!Figures 289 – Pages The Smart Port Market includes prominent Tier I and Tier II manufacturers like IBM, ABB, General Electric, Accenture, and Siemens. These companies have their manufacturing ... Tue, 09 Aug 2022 23:46:00 -0500 Tim Keary en-US text/html https://venturebeat.com/security/cybrary-cyber-skills-gap/ Killexams : Blockchain can change healthcare for the better. Here's how

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Killexams : IBM Expands Power10 Server Family to Help Clients Respond Faster to Rapidly Changing Business Demands

New Power10 scale-out and midrange models extend IBM's capabilities to deliver flexible and secured infrastructure for hybrid cloud environments

ARMONK, N.Y., July 12, 2022 /PRNewswire/ -- IBM (NYSE: IBM) today announced a significant expansion of its Power10 server line with the introduction of mid-range and scale-out systems to modernize, protect and automate business applications and IT operations. The new Power10 servers combine performance, scalability, and flexibility with new pay-as-you-go consumption offerings for clients looking to deploy new services quickly across multiple environments.

IBM Corporation logo. (PRNewsfoto/IBM)

IBM announced an expansion of its Power10 server line with mid-range and scale-out systems.

 Digital transformation is driving organizations to modernize both their applications and IT infrastructures. IBM Power systems are purpose-built for today's demanding and dynamic business environments, and these new systems are optimized to run essential workloads such as databases and core business applications, as well as maximize the efficiency of containerized applications. An ecosystem of solutions with Red Hat OpenShift also enables IBM to collaborate with clients, connecting critical workloads to new, cloud-native services designed to maximize the value of their existing infrastructure investments.

The new servers join the popular Power10 E1080 server introduced in September 2021 to deliver a secured, resilient hybrid cloud experience that can be managed with other x86 and multi-cloud management software across clients' IT infrastructure. This expansion of the IBM Power10 family with the new midrange and scale-out servers brings high-end server capabilities throughout the product line. Not only do the new systems support critical security features such as transparent memory encryption and advanced processor/system isolation, but also leverage the OpenBMC project from the Linux Foundation for high levels of security for the new scale-out servers.  

Highlights of the announcements include:

  • New systems: The expanded IBM Power10 portfolio, built around the next-generation IBM Power10 processor with 2x more cores and more than 2x memory bandwidth than previous Power generations, now includes the Power10 Midrange E1050, delivering record-setting 4-socket compute1, Java2, and ERP3 performance capabilities. New scale-out servers include the entry-level Power S1014, as well as S1022, and S1024 options, bringing enterprise capabilities to SMBs and remote-office/branch office environments, such as Capacity Upgrade on Demand (CuOD).
  • Cloud on premises with new flexible consumption choices: IBM has recently announced new flexible consumption offerings with pay-as-you-go options and by-the-minute metering for IBM Power Private Cloud, bringing more opportunities to help lower the cost of running OpenShift solutions on Power when compared against alternative platforms. These new consumption models build on options already available with IBM Power Virtual Server to enable greater flexibility in clients' hybrid journeys. Additionally, the highly anticipated IBM i subscription delivers a comprehensive platform solution with the hardware, software and support/services included in the subscription service.
  • Business transformation with SAP®: IBM continues its innovations for SAP solutions. The new midrange E1050 delivers scale (up to 16 TB) and performance for a 4-socket system for clients who run BREAKTHROUGH with IBM for RISE with SAP. In addition, an expansion of the premium provider option is now available to provide more flexibility and computing power with an additional choice to run workloads on IBM Power on Red Hat Enterprise Linux on IBM Cloud.

"Today's highly dynamic environment has created volatility, from materials to people and skills, all of which impact short-term operations and long-term sustainability of the business," said Steve Sibley, Vice President, IBM Power Product Management. "The right IT investments are critical to business and operational resilience. Our new Power10 models offer clients a variety of flexible hybrid cloud choices with the agility and automation to best fit their needs, without sacrificing performance, security or resilience."

The expansion of the IBM Power10 family has been engineered to establish one of the industry's most flexible and broadest range of servers for data-intensive workloads such as SAP S/4HANA – from on-premises workloads to hybrid cloud. IBM now offers more ways to implement dynamic capacity – with metering across all operating environments including IBM i, AIX, Linux and OpenShift supporting modern and traditional applications on the same platforms – as well as integrated infrastructure automation software for improved visibility and management.

The new systems with IBM Power Virtual Server also help clients operate a secured hybrid cloud experience that delivers high performance and architectural consistency across their IT infrastructure. The systems are uniquely designed so as to protect sensitive data from core to cloud, and enable virtual machines and containerized workloads to run simultaneously on the same systems. For critical business workloads that have traditionally needed to reside on-premises, they can now be moved into the cloud as workloads and needs demand. This flexibility can help clients mitigate risk and time associated with rewriting applications for a different platform.

"As organizations around the world continue to adapt to unpredictable changes in consumer behaviors and needs, they need a platform that can deliver their applications and insights securely where and when they need them," said Peter Rutten, IDC Worldwide Infrastructure Research Vice President. "IBM Power continues its laser focus on helping clients respond faster to dynamically changing environments and business demands, while protecting information security and distilling new insights from data, all with high reliability and availability."

Ecosystem of ISVs and Channel Partners Enhance Capabilities for IBM Power10

Critical in the launch of the expanded Power10 family is a robust ecosystem of ISVs, Business Partners, and lifecycle services. Ecosystem partners such as SVA and Solutions II provide examples of how the IBM Ecosystem collaborates with clients to build hybrid environments, connecting essential workloads to the cloud to maximize the value of their existing infrastructure investments:

"SVA customers have appreciated the enormous flexibility of IBM Power systems through Capacity Upgrade On-Demand in the high-end systems for many years," said Udo Sachs, Head of Competence Center Power Systems at SVA. "The flexible consumption models using prepaid capacity credits have been well-received by SVA customers, and now the monthly pay-as-you-go option for the scale-out models makes the platform even more attractive. When it comes to automation, IBM helps us to roll out complex workloads such as entire SAP landscapes at the push of a button by supporting Ansible on all OS derivatives, including AIX, IBM i and Linux, as well as ready-to-use modules for deploying the complete Power infrastructure."

"Solutions II provides technology design, deployment, and managed services to hospitality organizations that leverage mission critical IT infrastructure to execute their mission, often requiring 24/7 operation," said Dan Goggiano, Director of Gaming, Solutions II. "System availability is essential to maintaining our clients' revenue streams, and in our experience, they rely on the stability and resilience of IBM Power systems to help solidify their uptime. Our clients are excited that the expansion of the Power10 family further extends these capabilities and bolsters their ability to run applications securely, rapidly, and efficiently." 

For more information on IBM Power and the new servers and consumption models announced today, visit: https://www.ibm.com/it-infrastructure/power

About IBM

IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com.

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE in Germany and other countries. Please see https://www.sap.com/copyright for additional trademark information and notices.

1Comparison based on best performing 4-socket systems (IBM Power E1050 3.15-3.9 GHz, 96 core and Inspur NF8480M6 2.90 GHz, Intel Xeon Platinum 8380H) using published results at https://www.spec.org/cpu2017/results/rint2017.html as of 22 June 2022. For more information about SPEC CPU 2017, see https://www.spec.org/cpu2017/.

2Comparison based on best performing 4-socket systems (IBM Power E1050 3.15-3.9 GHz, 96 core; and Inspur NF8480M6 2.90 GHz, Intel Xeon Platinum 8380H) using published results at https://www.spec.org/cpu2017/results/rint2017.html as of 22 June 2022. For more information about SPEC CPU 2017, see www. http:/spec.org/cpu2017

3Comparison based on best performing 4-socket systems (1) IBM Power E1050; two-tier SAP SD standard application benchmark running SAP ERP 6.0 EHP5; Power10 2.95 GHz processor, 4,096 GB memory, 4p/96c/768t, 134,016 SD benchmark users, 736,420 SAPS, AIX 7.3, DB2 11.5,  Certification # 2022018  and (2) Dell EMC PowerEdge 840; two-tier SAP SD standard application benchmark running SAP ERP 6.0 EHP5; Intel Xeon Platinum 8280 2.7 GHz, 4p/112c/224t, 69,500 SD benchmark users (380,280 SAPS), SUSE Linux Enterprise Server 12 and SAP ASE 16, Certification # 2019045. All results can be found at sap.com/benchmark Valid as of 7 July 2022. 

Contact:
Ben Stricker
ben.stricker@ibm.com

Cision View original content to obtain multimedia:https://www.prnewswire.com/news-releases/ibm-expands-power10-server-family-to-help-clients-respond-faster-to-rapidly-changing-business-demands-301584186.html

SOURCE IBM

Mon, 11 Jul 2022 17:31:00 -0500 en-US text/html https://wgnradio.com/business/press-releases/cision/20220712NY11708/ibm-expands-power10-server-family-to-help-clients-respond-faster-to-rapidly-changing-business-demands/
Killexams : Enterprise Asset Management Market Report 2022-2030: Featuring Key Players Oracle, Rockwell Automation, IBM & Others - ResearchAndMarkets.com

Press release content from Business Wire. The AP news staff was not involved in its creation.

DUBLIN--(BUSINESS WIRE)--Jul 27, 2022--

The “Enterprise Asset Management Market Research Report - Global Industry Analysis and Growth Forecast to 2030” report has been added to ResearchAndMarkets.com’s offering.

The global enterprise asset management market 2030 value will likely be $21,471.3 million, growing from $5,682.1 million in 2020 at a 14.2% CAGR between 2020 and 2030. This will majorly be because EAM helps in reducing operational and maintenance costs and increasing the return on assets (ROA). This is done by tracking operations, using advanced maintenance solutions for effective equipment control, decreasing material procurement costs, and offering better insights into capital investment decisions.

EAM includes solutions for asset lifecycle management, work order management, inventory management, labor management, facility management, predictive maintenance, and reporting and analytics, which increase the productivity of employees, prevent unplanned system breakdowns (thereby minimizing maintenance costs), and offer a better return on investments (ROI).

The demand for EAM integration & deployment, consulting, training, and monitoring & upgradation services will rise fast in the coming years.

Due to the wide array of assets they own and their better financial stability, large enterprises have contributed the higher revenue to enterprise asset management market players till now.

In the future, the preference for cloud-based EAM solutions will rise faster with the increasing demand for anytime, anywhere data access, scalability, and reduced IT expenses.

The manufacturing sector is the largest user of EAM solutions because it encompasses a large number of factories, laborers, warehouses, and machines.

Key syllabus Covered:

Chapter 1. Research Background

Chapter 2. Research Methodology

Chapter 3. Executive Summary

Chapter 4. Introduction

Chapter 5. Global Market Size and Forecast

Chapter 6. North America Market Size and Forecast

Chapter 7. Europe Market Size and Forecast

Chapter 8. Apac Market Size and Forecast

Chapter 9. Latam Market Size and Forecast

Chapter 10. Mea Market Size and Forecast

Chapter 11. Major Countries

Chapter 12. Competitive Landscape

Chapter 13. Company Profiles

Chapter 14. Appendix

Companies Mentioned

  • Oracle Corporation
  • SAP SE
  • IBM Corporation
  • ABB Ltd.
  • Industrial and Financial Systems (IFS) AB
  • MRI Software LLC
  • CGI Inc.
  • Infor Inc.
  • Ramco Systems Limited
  • Rockwell Automation Inc.
  • AssetWorks LLC
  • UpKeep Technologies Inc.
  • Ultimo Software Solutions bv
  • Maintenance Connection LLC
  • eMaint Enterprises LLC
  • DataMAX Software Group Inc.

For more information about this report visit https://www.researchandmarkets.com/r/tzchlk

View source version on businesswire.com:https://www.businesswire.com/news/home/20220727005496/en/

CONTACT: ResearchAndMarkets.com

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KEYWORD:

INDUSTRY KEYWORD: SOFTWARE TECHNOLOGY ASSET MANAGEMENT PROFESSIONAL SERVICES

SOURCE: Research and Markets

Copyright Business Wire 2022.

PUB: 07/27/2022 06:42 AM/DISC: 07/27/2022 06:42 AM

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Killexams : The Changing Face of Incident Response

This article appeared in Cybersecurity Law & Strategy, an ALM publication for privacy and security professionals, Chief Information Security Officers, Chief Information Officers, Chief Technology Officers, Corporate Counsel, Internet and Tech Practitioners, In-House Counsel. Visit the website to learn more.

The drumbeat of cyberattacks is beating in an ever-faster cadence and the legal community is no exception. According to the American Bar Association, in 2020 alone, 29% of surveyed law firms said they experienced some type of cyber attack, an increase from 2019.

Tue, 19 Jul 2022 08:22:00 -0500 en text/html https://www.law.com/2022/07/19/the-changing-face-of-incident-response/?slreturn=20220710080156
Killexams : IBM Expands Power10 Server Family to Help Clients Respond Faster to Rapidly Changing Business Demands

New Power10 scale-out and midrange models extend IBM's capabilities to deliver flexible and secured infrastructure for hybrid cloud environments

ARMONK, N.Y., July 12, 2022 /PRNewswire/ -- IBM IBM today announced a significant expansion of its Power10 server line with the introduction of mid-range and scale-out systems to modernize, protect and automate business applications and IT operations. The new Power10 servers combine performance, scalability, and flexibility with new pay-as-you-go consumption offerings for clients looking to deploy new services quickly across multiple environments.

IBM announced an expansion of its Power10 server line with mid-range and scale-out systems.

 Digital transformation is driving organizations to modernize both their applications and IT infrastructures. IBM Power systems are purpose-built for today's demanding and dynamic business environments, and these new systems are optimized to run essential workloads such as databases and core business applications, as well as maximize the efficiency of containerized applications. An ecosystem of solutions with Red Hat OpenShift also enables IBM to collaborate with clients, connecting critical workloads to new, cloud-native services designed to maximize the value of their existing infrastructure investments.

The new servers join the popular Power10 E1080 server introduced in September 2021 to deliver a secured, resilient hybrid cloud experience that can be managed with other x86 and multi-cloud management software across clients' IT infrastructure. This expansion of the IBM Power10 family with the new midrange and scale-out servers brings high-end server capabilities throughout the product line. Not only do the new systems support critical security features such as transparent memory encryption and advanced processor/system isolation, but also leverage the OpenBMC project from the Linux Foundation for high levels of security for the new scale-out servers.  

Highlights of the announcements include:

  • New systems: The expanded IBM Power10 portfolio, built around the next-generation IBM Power10 processor with 2x more cores and more than 2x memory bandwidth than previous Power generations, now includes the Power10 Midrange E1050, delivering record-setting 4-socket compute1, Java2, and ERP3 performance capabilities. New scale-out servers include the entry-level Power S1014, as well as S1022, and S1024 options, bringing enterprise capabilities to SMBs and remote-office/branch office environments, such as Capacity Upgrade on Demand (CuOD).
  • Cloud on premises with new flexible consumption choices: IBM has recently announced new flexible consumption offerings with pay-as-you-go options and by-the-minute metering for IBM Power Private Cloud, bringing more opportunities to help lower the cost of running OpenShift solutions on Power when compared against alternative platforms. These new consumption models build on options already available with IBM Power Virtual Server to enable greater flexibility in clients' hybrid journeys. Additionally, the highly anticipated IBM i subscription delivers a comprehensive platform solution with the hardware, software and support/services included in the subscription service.
  • Business transformation with SAP®: IBM continues its innovations for SAP solutions. The new midrange E1050 delivers scale (up to 16 TB) and performance for a 4-socket system for clients who run BREAKTHROUGH with IBM for RISE with SAP. In addition, an expansion of the premium provider option is now available to provide more flexibility and computing power with an additional choice to run workloads on IBM Power on Red Hat Enterprise Linux on IBM Cloud.

"Today's highly dynamic environment has created volatility, from materials to people and skills, all of which impact short-term operations and long-term sustainability of the business," said Steve Sibley, Vice President, IBM Power Product Management. "The right IT investments are critical to business and operational resilience. Our new Power10 models offer clients a variety of flexible hybrid cloud choices with the agility and automation to best fit their needs, without sacrificing performance, security or resilience."

The expansion of the IBM Power10 family has been engineered to establish one of the industry's most flexible and broadest range of servers for data-intensive workloads such as SAP S/4HANA – from on-premises workloads to hybrid cloud. IBM now offers more ways to implement dynamic capacity – with metering across all operating environments including IBM i, AIX, Linux and OpenShift supporting modern and traditional applications on the same platforms – as well as integrated infrastructure automation software for improved visibility and management.

The new systems with IBM Power Virtual Server also help clients operate a secured hybrid cloud experience that delivers high performance and architectural consistency across their IT infrastructure. The systems are uniquely designed so as to protect sensitive data from core to cloud, and enable virtual machines and containerized workloads to run simultaneously on the same systems. For critical business workloads that have traditionally needed to reside on-premises, they can now be moved into the cloud as workloads and needs demand. This flexibility can help clients mitigate risk and time associated with rewriting applications for a different platform.

"As organizations around the world continue to adapt to unpredictable changes in consumer behaviors and needs, they need a platform that can deliver their applications and insights securely where and when they need them," said Peter Rutten, IDC Worldwide Infrastructure Research Vice President. "IBM Power continues its laser focus on helping clients respond faster to dynamically changing environments and business demands, while protecting information security and distilling new insights from data, all with high reliability and availability."

Ecosystem of ISVs and Channel Partners Enhance Capabilities for IBM Power10

Critical in the launch of the expanded Power10 family is a robust ecosystem of ISVs, Business Partners, and lifecycle services. Ecosystem partners such as SVA and Solutions II provide examples of how the IBM Ecosystem collaborates with clients to build hybrid environments, connecting essential workloads to the cloud to maximize the value of their existing infrastructure investments:

"SVA customers have appreciated the enormous flexibility of IBM Power systems through Capacity Upgrade On-Demand in the high-end systems for many years," said Udo Sachs, Head of Competence Center Power Systems at SVA. "The flexible consumption models using prepaid capacity credits have been well-received by SVA customers, and now the monthly pay-as-you-go option for the scale-out models makes the platform even more attractive. When it comes to automation, IBM helps us to roll out complex workloads such as entire SAP landscapes at the push of a button by supporting Ansible on all OS derivatives, including AIX, IBM i and Linux, as well as ready-to-use modules for deploying the complete Power infrastructure."

"Solutions II provides technology design, deployment, and managed services to hospitality organizations that leverage mission critical IT infrastructure to execute their mission, often requiring 24/7 operation," said Dan Goggiano, Director of Gaming, Solutions II. "System availability is essential to maintaining our clients' revenue streams, and in our experience, they rely on the stability and resilience of IBM Power systems to help solidify their uptime. Our clients are excited that the expansion of the Power10 family further extends these capabilities and bolsters their ability to run applications securely, rapidly, and efficiently." 

For more information on IBM Power and the new servers and consumption models announced today, visit: https://www.ibm.com/it-infrastructure/power

About IBM

IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com.

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE in Germany and other countries. Please see https://www.sap.com/copyright for additional trademark information and notices.

1Comparison based on best performing 4-socket systems (IBM Power E1050 3.15-3.9 GHz, 96 core and Inspur NF8480M6 2.90 GHz, Intel Xeon Platinum 8380H) using published results at https://www.spec.org/cpu2017/results/rint2017.html as of 22 June 2022. For more information about SPEC CPU 2017, see https://www.spec.org/cpu2017/.

2Comparison based on best performing 4-socket systems (IBM Power E1050 3.15-3.9 GHz, 96 core; and Inspur NF8480M6 2.90 GHz, Intel Xeon Platinum 8380H) using published results at https://www.spec.org/cpu2017/results/rint2017.html as of 22 June 2022. For more information about SPEC CPU 2017, see www. http:/spec.org/cpu2017

3Comparison based on best performing 4-socket systems (1) IBM Power E1050; two-tier SAP SD standard application benchmark running SAP ERP 6.0 EHP5; Power10 2.95 GHz processor, 4,096 GB memory, 4p/96c/768t, 134,016 SD benchmark users, 736,420 SAPS, AIX 7.3, DB2 11.5,  Certification # 2022018  and (2) Dell EMC PowerEdge 840; two-tier SAP SD standard application benchmark running SAP ERP 6.0 EHP5; Intel Xeon Platinum 8280 2.7 GHz, 4p/112c/224t, 69,500 SD benchmark users (380,280 SAPS), SUSE Linux Enterprise Server 12 and SAP ASE 16, Certification # 2019045. All results can be found at sap.com/benchmark Valid as of 7 July 2022. 

Contact:
Ben Stricker
ben.stricker@ibm.com

View original content to obtain multimedia:https://www.prnewswire.com/news-releases/ibm-expands-power10-server-family-to-help-clients-respond-faster-to-rapidly-changing-business-demands-301584186.html

SOURCE IBM

© 2022 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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