#watsonx platform

[sonalikrishnan]

Pragma Edge Provides services on watsonx

Pragma Edge Provides services on watsonx, it is an innovative Artificial Intelligence (AI) and Data Platform designed to enable enterprises with a focus of scale and accelerating the impact of AI capabilities with trusted data. This Platform offers a comprehensive solution that covers data storage, hardware, and foundational models for Artificial Intelligence (AI) and Machine Learning (ML). This platform provides an open ecosystem, enabling enterprises to design and tune large language models (LLMs) for operational and business requirements. 

IBM’s watsonx Platform helps enterprises automate their business workflows, streamlining IT processes and internal business processes, protecting them against threats and vulnerabilities, and tackling sustainability goals. It also includes a data store, built on lakehouse architecture, and an AI governance toolkit. 

watsonx.ai

watsonx.ai is an enterprise development studio that will enable AI builders to train, test, tune, and deploy traditional machine learning and new generative AI capabilities across their enterprise business that leverage the power of foundation models.

This provides a wide range of foundation models, training and tuning tools, and cost-effective infrastructure that can facilitate the entire data & AI lifecycle process, from data preparation to model development, monitoring, and deployment.

watsonx.data

watsonx.data is a fit-for purpose data store built on open lakehouse architecture offering, focuses on analytics and AI workloads. As data is what helps AI learn and grow, this component is critical for enabling AI capabilities. 

This open and agile data repository enables enterprises to efficiently manage and access massive amounts of data. It will support their AI initiatives and facilitate quick decision-making processes.

watsonx.governance

watsonx.governance provides the framework for creating an AI workforce that operates responsibly and with transparency. This component establishes guidelines for explainable AI, ensuring that businesses can understand the decisions made by AI models and build trust with their clients, partners etc. 

It has seamless integrated capabilities that can augment your existing Machine Learning(ML) development and deployment with governance. You can able access all the information data across the on-premises and cloud environments.

Improve data access, apply governance, cut costs, and get quality models into production faster.

Accelerate the whole AI model lifecycle by having all of the tools and runtimes for training, validating, tuning, and deploying AI models in one location.

[jcmarchi]

AI in 2025: Purpose-driven models, human integration, and more

New Post has been published on https://thedigitalinsider.com/ai-in-2025-purpose-driven-models-human-integration-and-more/

AI in 2025: Purpose-driven models, human integration, and more

As AI becomes increasingly embedded in our daily lives, industry leaders and experts are forecasting a transformative 2025.

From groundbreaking developments to existential challenges, AI’s evolution will continue to shape industries, change workflows, and spark deeper conversations about its implications.

For this article, AI News caught up with some of the world’s leading minds to see what they envision for the year ahead.

Smaller, purpose-driven models

Grant Shipley, Senior Director of AI at Red Hat, predicts a shift away from valuing AI models by their sizeable parameter counts.

“2025 will be the year when we stop using the number of parameters that models have as a metric to indicate the value of a model,” he said.  

Instead, AI will focus on specific applications. Developers will move towards chaining together smaller models in a manner akin to microservices in software development. This modular, task-based approach is likely to facilitate more efficient and bespoke applications suited to particular needs.

Open-source leading the way

Bill Higgins, VP of watsonx Platform Engineering and Open Innovation at IBM, expects open-source AI models will grow in popularity in 2025.

“Despite mounting pressure, many enterprises are still struggling to show measurable returns on their AI investments—and the high licensing fees of proprietary models is a major factor. In 2025, open-source AI solutions will emerge as a dominant force in closing this gap,” he explains.

Alongside the affordability of open-source AI models comes transparency and increased customisation potential, making them ideal for multi-cloud environments. With open-source models matching proprietary systems in power, they could offer a way for enterprises to move beyond experimentation and into scalability.

This plays into a prediction from Nick Burling, SVP at Nasuni, who believes that 2025 will usher in a more measured approach to AI investments. 

“Enterprises will focus on using AI strategically, ensuring that every AI initiative is justified by clear, measurable returns,” said Burling.

Cost efficiency and edge data management will become crucial, helping organisations optimise operations while keeping budgets in check.  

Augmenting human expertise

For Jonathan Siddharth, CEO of Turing, the standout feature of 2025 AI systems will be their ability to learn from human expertise at scale.

“The key advancement will come from teaching AI not just what to do, but how to approach problems with the logical reasoning that coding naturally cultivates,” he says.

Competitiveness, particularly in industries like finance and healthcare, will hinge on mastering this integration of human expertise with AI.  

Behavioural psychology will catch up

Understanding the interplay between human behaviour and AI systems is at the forefront of predictions for Niklas Mortensen, Chief Design Officer at Designit.

“With so many examples of algorithmic bias leading to unwanted outputs – and humans being, well, humans – behavioural psychology will catch up to the AI train,” explained Mortensen.  

The solutions? Experimentation with ‘pause moments’ for human oversight and intentional balance between automation and human control in critical operations such as healthcare and transport.

Mortensen also believes personal AI assistants will finally prove their worth by meeting their long-touted potential in organising our lives efficiently and intuitively.

Bridge between physical and digital worlds

Andy Wilson, Senior Director at Dropbox, envisions AI becoming an indispensable part of our daily lives.

“AI will evolve from being a helpful tool to becoming an integral part of daily life and work – offering innovative ways to connect, create, and collaborate,” Wilson says.  

Mobile devices and wearables will be at the forefront of this transformation, delivering seamless AI-driven experiences.

However, Wilson warns of new questions on boundaries between personal and workplace data, spurred by such integrations.

Driving sustainability goals 

With 2030 sustainability targets looming over companies, Kendra DeKeyrel, VP ESG & Asset Management at IBM, highlights how AI can help fill the gap.

DeKeyrel calls on organisations to adopt AI-powered technologies for managing energy consumption, lifecycle performance, and data centre strain.

“These capabilities can ultimately help progress sustainability goals overall,” she explains.

Unlocking computational power and inference

James Ingram, VP Technology at Streetbees, foresees a shift in computational requirements as AI scales to handle increasingly complex problems.

“The focus will move from pre-training to inference compute,” he said, highlighting the importance of real-time reasoning capabilities.

Expanding context windows will also significantly enhance how AI retains and processes information, likely surpassing human efficiency in certain domains.

Rise of agentic AI and unified data foundations

According to Dominic Wellington, Enterprise Architect at SnapLogic, “Agentic AI marks a more flexible and creative era for AI in 2025.”

However, such systems require robust data integration because siloed information risks undermining their reliability.

Wellington anticipates that 2025 will witness advanced solutions for improving data hygiene, integrity, and lineage—all vital for enabling agentic AI to thrive.  

From hype to reality

Jason Schern, Field CTO of Cognite, predicts that 2025 will be remembered as the year when truly transformative, validated generative AI solutions emerge.

“Through the fog of AI for AI’s sake noise, singular examples of truly transformative embedding of Gen AI into actual workflows will stand out,” predicts Schern.  

These domain-specific AI agents will revolutionise industrial workflows by offering tailored decision-making. Schern cited an example in which AI slashed time-consuming root cause analyses from months to mere minutes.

Deepfakes and crisis of trust

Sophisticated generative AI threatens the authenticity of images, videos, and information, warns Siggi Stefnisson, Cyber Safety CTO at Gen.

“Even experts may not be able to tell what’s authentic,” warns Stefnisson.

Combating this crisis requires robust digital credentials for verifying authenticity and promoting trust in increasingly blurred digital realities.

2025: Foundational shifts in the AI landscape

As multiple predictions converge, it’s clear that foundational shifts are on the horizon.

The experts that contributed to this year’s industry predictions highlight smarter applications, stronger integration with human expertise, closer alignment with sustainability goals, and heightened security. However, many also foresee significant ethical challenges.

2025 represents a crucial year: a transition from the initial excitement of AI proliferation to mature and measured adoption that promises value and a more nuanced understanding of its impact.

See also: AI Action Summit: Leaders call for unity and equitable development

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

[shreyash-hexa]

🌟 The Impact of AI on Software Development: Revolutionizing the Future! 🌟

Hey, tech enthusiasts! 🚀 Let’s dive into how Artificial Intelligence (AI) is transforming the world of software development. From automating tasks to enhancing code quality, AI is changing the game! Here’s how:

💻 Streamlining Coding Processes

AI tools are becoming essential in coding! Generative AI and large language models (LLMs) can turn natural language descriptions into code snippets or even entire functions. This means developers can focus on creative tasks instead of getting stuck on repetitive coding. Tools like IBM Watsonx Code Assistant™ and GitHub Copilot are here to boost productivity by suggesting code and automating routine tasks. Talk about a game-changer! ⚡

🔍 Enhancing Debugging and Testing

Debugging and testing just got a major upgrade! AI tools can automatically detect bugs, vulnerabilities, and inefficiencies in your code. They generate adaptive test cases that prioritize what’s most critical, improving software quality and security. Plus, AI can analyze historical data to predict potential errors—helping you tackle issues before they become headaches! 🛠️

📊 Improving Project Management

AI isn’t just for coding; it’s also a powerhouse for project management! AI-driven tools automate scheduling, resource management, and CI/CD processes. This means better time estimates, efficient resource allocation, and real-time performance monitoring. Say goodbye to project chaos! 🗂️✨

🔧 Facilitating Software Maintenance

Keeping software systems up-to-date can be tough, but AI makes it easier! By analyzing data, AI recommends upgrades and enhancements, ensuring your software stays relevant and efficient over time. It’s like having a personal assistant for your code! 📈

🌐 Enabling Intelligent Systems

AI is paving the way for intelligent software that learns from data and adapts to changes. Think AI-powered chatbots that improve their responses over time or recommendation systems that get smarter with user behavior. This adaptability enhances user experience like never before! 🤖💬

🔮 The Future of Software Development

Looking ahead, AI will continue to play a crucial role in software development. We might see new methodologies that redefine traditional practices like agile development. Developers who embrace this change will be at the forefront of innovation—where creativity meets automation! 🌈✨

Conclusion

AI in software development isn’t just about automation; it’s about enhancing our capabilities as developers. By streamlining processes, improving testing, optimizing project management, facilitating maintenance, and enabling intelligent systems, AI is transforming how we build software.

So, let’s embrace these advancements and step into a future where creativity and technology go hand in hand! 💡🚀

For more insights on how AI is shaping the future of software development, check out resources like IBM’s perspective on AI in Software Development or explore practical applications on platforms like GeeksforGeeks. Happy coding! 🎉

[govindhtech]

IBM Watsonx.ai Management Tools Release GenAI Possibility

IBM Watsonx.ai Management Tools Unleash GenAI Potential.

US regulators like the FRB, SEC, and OCC require financial services firms to show that their risk governance structure addresses laws, rules, and regulations (LRRs). This monitoring helps maintain a safe and dependable control environment that meets tighter rules and the organization’s risk tolerance.

However, determining the applicability of banking regulations to particular sections of a legislation can be a difficult and subjective process that calls for expert judgment. Based on the bank’s attributes, such as being a Global Systemically Important Bank (GSIB) or providing certain goods and services, banks frequently depend on outside suppliers to evaluate LRRs and generic controls.

Furthermore, LRRs are always changing, as are other industry frameworks like the Control Objectives for Information and Related Technologies (COBIT), Information Technology Infrastructure Library (ITIL), and National Institute of Standards and Technology (NIST).

This ongoing development necessitates constant work to help guarantee that the organization’s control environment is free of holes. Regretfully, it takes a lot of time and frequently causes delays to manually link LRRs to rules, standards, procedures, risk metrics, and controls. This procedure creates a discrepancy between the organization’s capacity to prove compliance with LRRs and regulatory expectations.

For instance, a bank may have a policy requiring the protection of its clients’ personal information, and the standard may call for the encryption of such information. In that scenario, the control would assist in guaranteeing that personal data is encrypted, and the procedure would specify the steps to encrypt it. However, the bank may not be able to prove compliance with the encryption standard, putting them at risk of noncompliance, if the links between LRRs and controls are not updated promptly.

The watsonx Regulatory Compliance Platform reduces manual effort for control owners, compliance, risk and legal teams

Legal and regulatory requirements can be mapped to a risk governance framework using IBM Watsonx, which also automates the identification of regulatory duties. This solution facilitates the verification of compliance with current responsibilities by examining governance documents and controls and connecting them to relevant LRRs. By using this technology, audit, compliance, risk, legal, IT, and business control owners can construct and maintain LRR libraries with a great deal less human labor.

For instance, Watson Discovery can undertake an effect analysis by actively searching the internet for regulatory revisions for a certain group of LRRs. Watson Assistant can be utilized as an interactive Q&A tool to answer questions from external parties, auditors, and regulators regarding the risk and control environment in a conversational fashion. A risk and compliance program is increasingly using large language model (LLM), which need little to no training.

To apply the banks’ different process, risk, and control taxonomies, LLMs stored in Watsonx augment LRR and governance data. A prompt evaluates an obligation using a programmed approach. For instance, every risk category of the company, including strategic, reputational, wholesale, interest rate, and liquidity risks, would be examined to see what applies. The matching categories to internal controls and other pertinent policy and governance datasets are supported by the improved metadata.

When the content is publicly accessible, whether from third parties or is curated by the organization in an obligation’s library, the procedure is uniform and repeatable across regulations. IT and cybersecurity frameworks like NIST, ITIL, COBIT, Cloud Security Alliance Control Matrix, Federal Financial Institutions Examination Council (FFIEC), and others are included in the mapping and coverage capabilities that are not exclusive to LRRs.

The solution may link the pertinent LRRs to the applicable NIST controls, for example, if a bank wishes to guarantee adherence to the NIST cybersecurity framework. This gives the bank a clear and thorough picture of its cybersecurity posture.

IBM Watsonx.ai

How the watsonx Regulatory Compliance Platform accelerates risk management

The platform’s advanced artificial intelligence (AI) modules, IBM Watsonx.ai, watsonx.gov, and watsonx.data, provide a variety of cutting-edge technical features tailored to the particular requirements of the sector. These components, which may be installed on-premises or in any cloud, are based on IBM’s cutting-edge AI technology.

Users can participate in the whole lifecycle management of generative AI (gen AI) solutions within the IBM Watsonx.ai platform, which includes training, validation, tuning, and deployment processes. Watsonx.ai supports a variety of natural and programming language use cases by facilitating the development of expanded language models through the usage of foundation models from IBM and other sources.

The platform includes the cutting-edge Prompt Lab tool, which was created especially to expedite prompt engineering procedures. By using pre-written sample prompts, customers may confidently start their regulatory and compliance projects quickly and save successful prompts as notebook entries or reusable resources.

Interestingly, the prompt engineering parameters, model references, and prompt wording are all carefully formatted as Python code inside notebooks, enabling smooth programmable interaction. Additionally, IBM Watsonx.ai provides the Tuning Studio function, which enables users to iteratively steer foundation models toward outputs that are more in line with their particular needs.

Watsonx.governance‘s comprehensive suite of tools allows customers to quickly construct responsible, transparent, and explainable AI workflows that are suited to both machine learning and generative AI models. When installed, watsonx.governance combines the features of AI factsheets and Watson OpenScale with the Model Risk Governance features of OpenPages into a single service.

Watsonx.governance also expands its governance features to include generative AI assets. This platform enables users to evaluate machine learning models and foundation model prompts, build AI use cases for the methodical tracking of solutions addressing relevant business concerns, and develop processes while precisely monitoring lifecycle activities.

By supporting data from various sources and removing the requirement for migration or cataloging through open formats, IBM Watsonx.data enables scalable analytics and AI initiatives. This method reduces data duplication and extract, transform, and load (ETL) operations while allowing centralized access and sharing. Data preparation for a variety of applications, including retrieval augmented generation (RAG) and other machine learning and generative AI use cases, is made easier by integrated vectorized embedding capabilities.

Without the need for SQL knowledge, a conversational interface driven by Gen AI makes data discovery, augmentation, and visualization easier. Interoperability is ensured by smooth interface with current data stacks, tools, and databases.

All things considered, using Watsonx for regulatory compliance provides a revolutionary method of transparently and responsibly managing risk and AI projects. Organizations may easily handle the intricacies of regulatory requirements by utilizing its full range of capabilities. This makes it easier to guarantee ethical AI practices throughout the whole lifecycle, from data management to model training. IBM Watsonx.ai enables users to confidently evaluate, track, and improve AI workflows, promoting creativity and confidence in AI-driven solutions while easing regulatory compliance.

Read more on Govindhtech.com

[prolificsinsightsblog]

Prolifics at IBM TechXchange 2024

IBM TechXchange 2024 Conference 

Dates: October 21-24, 2024  Location: Mandalay Bay – Las Vegas  Conference Website: https://www.ibm.com/  

We are speaking at IBM TechXchange Conference 2024 

Join Prolifics at this October at IBM TechXchange  for an immersive AI learning experience focused on unlocking the potential of generative AI   and maximizing IBM’s powerful technology suite. Engage in hands-on watsonx challenges, deep-dive technology breakouts, and immersive instructor-led labs to sharpen your skills, earn valuable credentials, and connect with top innovators shaping the future of technology.  

Meet Our Experts 

Amrith Maldonado, Product Support Manager, Prolifics  

Vishnu Pandit, Practice Director – Integration and Platforms, Prolifics 

Attend our sessions at IBM TechXchange Conference 2024 to discover how to accelerate your AI journey and stay at the forefront of industry innovation. Elevate your expertise while connecting with peers and industry leaders who are driving the future of technology. 

Our experts will cover key topics that matter to your business, including:  

Data Governance:  Discoverhow the MPP Connector enhances Data Governance by integrating Manta's advanced metadata and lineage capabilities with Microsoft Purview, ensuring comprehensive visibility and control.  

Reduce Technical debt with IBM’s Integration Portfolio: Learn how to leverage IBM’s integration portfolio’s advanced monitoring, seamless integration, automation, and governance tools to minimize technical debt and ensure long-term sustainable growth for your business.   

This conference is your must-attend event for connecting with AI developers, industry innovators, and others seeking the tools and knowledge to transform their work.  

We’re can’t wait to connect with you—see you there! 

About Prolifics  

Prolifics, in collaboration with IBM, leverages the power of watsonx to deliver innovative AI solutions that fuel business transformation. Together, we enable organizations to harness AI and automation to drive smarter decisions and faster, more impactful results.  

Join us at IBM TechXchange 2024 to explore how we can elevate your AI journey. 

[mediatechgroup]

What is WatsonX AI

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Understanding WatsonX AI: An Informational Guide for AI Enthusiasts

In the fast-paced world of Artificial Intelligence, new developments are always emerging. One standout innovation attracting attention from both AI enthusiasts and professionals is WatsonX AI. Created by IBM, WatsonX is revolutionizing AI with its unique offerings. But what exactly is WatsonX AI, and why is it so special?

The Legacy of IBM in Technology

IBM, or International Business Machines Corporation, has been a major player in the tech industry for decades. They've evolved from mainframes to modern cloud computing, always staying innovative. In the fields of Machine Learning (ML) and Artificial Intelligence (AI), IBM's contributions have been groundbreaking. Their Watson platform has been a game-changer, proving AI's potential in healthcare, customer service, and even entertainment.

Introduction to WatsonX AI

WatsonX is the next step in IBM's AI evolution. But what is WatsonX AI? It’s a suite of AI tools designed to make AI projects easier to implement and scale. Whether you’re a seasoned data scientist or just starting, WatsonX has solutions for everyone. From powerful Machine Learning models to customizable algorithms, WatsonX is setting new AI standards.

Key Features of WatsonX AI

Ease of Use: WatsonX is designed to be user-friendly, so you don’t need to be a coding expert to use it effectively.

Customizability: The platform lets you adjust algorithms and models to fit your specific needs.

Scalability: WatsonX easily scales from small projects to large enterprise applications.

Integration: It integrates seamlessly with other IBM platforms and tools, embedding AI into existing workflows smoothly.

WatsonX AI in Action

Seeing WatsonX AI in real-world scenarios can be eye-opening, especially for those new to AI. One remarkable use is in healthcare, where WatsonX analyzes large sets of medical data to help doctors make more accurate diagnoses and treatment plans. In the business world, companies use WatsonX to improve customer service by answering common questions and solving issues faster.

Media & Technology Group, LLC and WatsonX AI

At Media & Technology Group, LLC, we know how powerful AI can be in boosting business processes. By integrating WatsonX into our services like Artificial Intelligence Implementation and Marketing Automation, we’ve enhanced our offerings significantly. Our clients enjoy more efficient operations, better decision-making, and improved customer experiences.

Benefits of WatsonX AI

Time Efficiency: Automate routine tasks, freeing up time for more complex problem-solving.

Data Accuracy: Advanced algorithms ensure high accuracy in data analysis.

Cost Savings: Reducing the need for manual labor leads to significant savings over time.

Innovation: Pushes the boundaries of what’s possible, inspiring new ideas and solutions.

Getting Started with WatsonX AI

If you’re eager to explore WatsonX AI, here’s how to start:

Training: IBM offers courses and materials online to help you learn WatsonX’s basics and

[education30and40blog]

Eighteen generative AI tools transforming customer service

See on Scoop.it - Education 2.0 & 3.0

"Explore the top 18 generative AI tools revolutionizing customer service, from advanced chatbots like Cognigy and IBM WatsonX Assistant to comprehensive platforms ..."

[globalfintechseries]

Transforming IT Service Management Through AIOps

The 2022 Gartner Market Guide for AIOps Platforms states, “There is no future of IT service management that does not include AIOps.” This is certainly a confirmation of the increasing need for IT organizations to adopt AIOps to respond to the fast data growth.

Gartner reveals that AIOps has become the part and parcel of IT operations, and discussions on AIOps appear in 40% of all the inquiries within the last year regarding IT performance analysis. Three drivers are behind the growing interest in AIOps: digital business transformation, the shift from reactive to proactive IT management, and the need to make digital business operations observable.

IT customers are increasingly curious about how AIOps can help them control the growing complexity and volume of their data—issues that are beyond the capability of manual human intervention. As Gartner says, “It is humanly impossible to derive insights from the sheer volume of IT system events that reach several thousand per second without AIOps.”

Also Read: IBM Introduces New Updates to Watsonx Platform at THINK 2024

What is AIOps?

AIOps, or Artificial Intelligence for IT Operations, represents a modern approach to managing IT operations. It uses AI and machine learning to automate and optimize IT processes. By harnessing the pattern recognition abilities of AI and ML, AIOps can analyze data, detect patterns, make predictions, and even automate decision-making. When effectively implemented, this transformative technology can revolutionize traditional IT service management (ITSM) methods by reducing manual workloads, speeding up response times, and enabling proactive strategies to prevent IT issues before they arise.

AIOps and IT Service Management

Gartner believes that integrating ITSM is an important requirement of an effective AIOps strategy. Integration is one of the three-prong strategies for an AIOps: Observe (Monitor), Engage (ITSM), and Act (Automation). Gartner continues, “AIOps platforms enhance a broad range of IT practices, including I&O, DevOps, SRE, security, and service management.” Application of AI to service management, or AISM, is much more than traditional ITSM in that it enables proactive prevention, faster MTTR, rapid innovation, and improved employee and customer experiences.

This is where machine learning and analytics enable ITSM/ITOM convergence, a key characteristic of ServiceOps. An integrated AIOps strategy that observes, engages, and acts will facilitate a set of integrated use cases across ITOM and ITSM, such as automated event remediation, incident and change management, and intelligent ticketing and routing.

https://1931032958.rsc.cdn77.org/wp-content/uploads/2024/05/Aiops-Platform.png

The ability to derive actionable insights based on machine learning and data analytics will bring significant value to IT operations teams. Successful implementation requires robust integrations with orchestration tools and the Configuration Management Database (CMDB) for service impact mapping. Visibility, intelligence, speed, and insights brought about by AIOps will be transformative in monitoring processes, bringing substantial benefits.

How to Implement AIOps for IT Service Management?

First and foremost, to onboard AIOps in ITSM, one should establish clear goals and define KPIs. The selection of the AIOps solution should support these objectives. Integrate different data sources, tune machine learning models, and integrate new processes with ITSM workflows.

Overcome the challenges of data silos, resistance to change, and shortage of skilled people through good cross-functional collaboration and continuous learning programs. The implementation should be done in a phased manner. Start with small, manageable projects and keep fine-tuning according to the feedback.

AIOps Benefits for ITSM

AIOps solutions automate incident detection and resolution processes. Utilizing AI-powered tools to monitor system metrics and logs, IT teams can predict and proactively address potential issues well before they result in outages and result in reduced downtime and better service availability.

Intelligent Root Cause Analysis: AIOps deploys state-of-the-art ML algorithms to analyze mountains of data from numerous sources efficiently, finding the root cause of incidents in the fastest way possible.

Predictive Maintenance: AIOps uses historical data and real-time analytics to predict system failures and performance degradation, allowing proactive maintenance actions.

Improved Data Management: AIOps makes the data management process much easier by consolidating data from log files, monitoring tools, and ticketing systems, making handling and analysis of data much easier and smoother.

Also Read: AI at Workplace: Essential Steps for CIOs and Security Teams

Future Outlook

AIOps is not a trend but the future of IT Service Management. As AIOps evolves, it will lead to huge changes in ITSM: complete automation of routine tasks, more accurate predictions, and increased business process integration. Keeping informed of these developments and preparing to adapt is vital in keeping ITSM future-ready.

Integrating AIOps and predictive analysis can transform ITSM by making proactive issue management, efficiency, and data-driven decision-making possible. The benefits are huge, including reducing manual loads, shortening response time, and improving service quality and business alignment. With AIOps and predictive analysis, businesses will continue to be competitive, innovate, and deliver outstanding IT services in today’s digitally enabled world.

[systemtek]

IBM Completes Acquisition of StreamSets and webMethods, Bolstering its Automation, Data and AI Portfolios

IBM announced it has completed its acquisition of StreamSets and webMethods from Software AG after receiving all required regulatory approvals. The acquisition brings together leading capabilities in integration, API management and data ingestion.  The acquisition builds on IBM's extensive software portfolio, with StreamSets adding new data ingestion capabilities to IBM's AI and data platform, and webMethods bringing Integration Platform as a Service (iPaas) capabilities to IBM's Automation solutions. IBM's clients and partners will now have access to one of the most modern and comprehensive application and data integration platforms in the industry to drive innovation and prepare business for AI.  Organizations are facing an explosion of apps, APIs, events and data spread across hybrid cloud environments worldwide. In fact, IDC estimates that 1 billion new applications are expected by 2028 due to the rapid emergence of generative AI1. As organizations continue their digital transformation journeys, application and data integration solutions are critical for application modernization and effectively deploying AI across the enterprise. StreamSets and webMethods currently serve more than 1,500 companies across the globe and will provide additional integration technologies for next-generation AI applications that require data and connectivity to everything, everywhere. And, the industry is continuing to grow, with IDC predicting the worldwide integration software market to exceed $18.0 billion in 20272. "This is an important acquisition for IBM as we help our clients turn complexities into competitive advantage," said Dinesh Nirmal, Senior Vice President, Products, IBM Software. "StreamSets and webMethods bring new capabilities to our clients to embrace data and AI to better manage the growth and complexity of applications. We will empower integrators, developers, and line of business IT to build and manage integrations at an even greater and more impactful scale."  StreamSets adds cloud-based, real-time data ingestion capabilities for various types of data to watsonx, IBM's AI and data platform.  Data ingestion helps move massive amounts of data from multiple sources to a centralized storage center where it can then be utilized by other systems/applications. When that data moves between sources and targets, streaming tools like StreamSets provide updated data in real-time to target destinations. This hybrid and multi-cloud ready product, which IBM plans to embed as a premium feature in watsonx.data, makes it easier for users to ingest, enrich, and harness the potential of streaming data enabled through features like offset handling and delivery guarantees. StreamSets will also further extend the breadth and depth of IBM's Data Fabric and Data Integration capabilities through enabling the design of streaming data pipelines. It will complement IBM DataStage and Databand into a deeply integrated offering for data engineers, catering to multiple patterns of data integrated, infused with data observability capabilities. IBM plans to make StreamSets available across all major hyperscalers, including GCP (current) and Azure/AWS (in-progress), as well as on-premises.  webMethods helps organizations manage the tangled web of systems, applications and data silos within business environments. The webMethods Integration Platform as a Service (iPaaS) enables users to deploy and execute integrations anywhere, while still including outputs in unified integration flows. This helps global organizations meet local data sovereignty requirements while driving enterprise-wide innovation and taking advantage of centralized management.  IBM plans to extend the webMethods iPaaS to support the IBM integration products, giving current customers a path to multi-cloud hybrid integration.  By supporting various patterns of integration, including applications, APIs, events, and B2B, IBM will help enable users to compose modern, unified, and seamless applications and services. For more information on StreamSets, click here.  For more information on webMethods, click here.  Read the full article

[sonalikrishnan]

Unlocking the Future of AI and Data with Pragma Edge's Watson X Platform  

In a rapidly evolving digital landscape, enterprises are constantly seeking innovative solutions to harness the power of artificial intelligence (AI) and data to gain a competitive edge. Enter Pragma Edge's Watson X, a groundbreaking AI and Data Platform designed to empower enterprises with scalability and accelerate the impact of AI capabilities using trusted data. This comprehensive platform offers a holistic solution, encompassing data storage, hardware, and foundational models for AI and Machine Learning (ML). 

The All-in-One Solution for AI Advancement 

At the heart of Watson X is its commitment to providing an open ecosystem, allowing enterprises to design and fine-tune large language models (LLMs) to meet their operational and business requirements. This platform is not just about AI; it's about transforming your business through automation, streamlining workflows, enhancing security, and driving sustainability goals. 

Key Components of Watson X 

Watsonx.ai: The AI Builder's Playground 

Watsonx.ai is an enterprise development studio where AI builders can train, test, tune, and deploy both traditional machine learning and cutting-edge generative AI capabilities. 

It offers a diverse array of foundation models, training and tuning tools, and cost-effective infrastructure to facilitate the entire data and AI lifecycle. 

Watsonx.data: Fueling AI Initiatives 

Watsonx.data is a specialized data store built on the open lakehouse architecture, tailored for analytics and AI workloads. 

This agile and open data repository empowers enterprises to efficiently manage and access vast amounts of data, driving quick decision-making processes. 

Watsonx.governance: Building Responsible AI 

Watsonx.governance lays the foundation for an AI workforce that operates responsibly and transparently. 

It establishes guidelines for explainable AI, ensuring businesses can understand AI model decisions, fostering trust with clients and partners. 

Benefits of WatsonX 

Unified Data Access: Gain access to information data across both on-premises and cloud environments, streamlining data management. 

Enhanced Governance: Apply robust governance measures, reduce costs, and accelerate model deployment, ensuring high-quality outcomes. 

End-to-End AI Lifecycle: Accelerate the entire AI model lifecycle with comprehensive tools and runtimes for training, validation, tuning, and deployment—all in one location. 

In a world driven by data and AI, Pragma Edge's Watson X Platform empowers enterprises to harness the full potential of these technologies. Whether you're looking to streamline processes, enhance security, or unlock new business opportunities, Watson X is your partner in navigating the future of AI and data. Don't miss out on the transformative possibilities—explore Watson X today at watsonx.ai and embark on your journey towards AI excellence. 

Learn more: https://pragmaedge.com/watsonx/ 

[jcmarchi]

AI solutions: Lessons from the generative AI summit

New Post has been published on https://thedigitalinsider.com/ai-solutions-lessons-from-the-generative-ai-summit/

AI solutions: Lessons from the generative AI summit

At the Generative AI Summit in Toronto, we had the chance to sit down with Manav Gupta, VP and CTO at IBM Canada, for a quick but insightful chat on IBM’s leadership in generative AI. From groundbreaking projects to industry-wide transformation, here are the key takeaways from our conversation.

Or you can check out the full interview right here:

IBM’s Approach to Generative AI

IBM isn’t just riding the generative AI wave—they’re shaping it. According to Manav, IBM believes that enterprises must own their AI agenda and that AI should be open, accessible, and built with governance at its core.

Their secret weapon? Watsonx, a platform that gives users access to IBM’s models, third-party models, and tools to fine-tune AI for their needs. Whether deployed on the cloud or on-premises, Watsonx aims to provide flexibility while ensuring AI remains responsible and enterprise-ready.

Speaking of responsibility, AI governance is another major focus. IBM is tackling critical issues like bias, misinformation, and ethical concerns to make sure AI outputs are free of hate, abuse, and biases. In short—powerful AI, but with guardrails.

How generative AI is transforming industries

Manav didn’t hold back on the impact AI is having across sectors. From banking to healthcare, public sector to telecoms, generative AI is unlocking efficiencies by handling repetitive tasks, allowing humans to focus on higher-value work.

And the numbers speak for themselves—some analysts predict AI could add up to 3.5 basis points to global GDP. That’s no small feat.

The biggest hurdles in AI implementation

Of course, with great potential comes great challenges. Manav highlighted three key roadblocks in deploying generative AI at scale:

Maturity of the technology – Enterprises are still in the experimentation phase, figuring out how to best use AI.

Integration with existing systems – AI doesn’t exist in a vacuum. Many companies struggle with data silos, making it difficult to leverage AI effectively across departments.

Resource availability – Running AI at scale requires specialized (and expensive) hardware with long lead times for procurement.

These challenges aren’t insurmountable, but they do require careful strategy and investment.

What’s next for generative AI?

So, where is the industry heading? According to Manav, we’re moving toward:

Smaller, fit-for-purpose AI models instead of massive, general-purpose ones.

Agentic AI, where AI takes on tasks with greater autonomy, especially in high-value fields like software engineering and testing.

Multimodal AI, allowing models to process multiple types of data—think image-to-text translations and AI making contextual decisions based on various inputs.

Manav’s three big takeaways

Before heading off to answer more audience questions, Manav left us with three key lessons from his talk:

Be an AI value creator, not just a consumer. Don’t just use AI—figure out how to make it work for you.

Start with models you can trust. Whether it’s IBM’s Granite models or open-source alternatives, experiment with reliable AI solutions.

Don’t treat AI governance as an afterthought. Privacy, security, and responsible AI should be built into the foundation of your AI strategy.

Final thoughts

Manav’s insights were a reminder that while generative AI is a game-changer, it’s only as powerful as the way we use and govern it. With the right approach, AI isn’t just a tool—it’s a transformation engine.

Stay tuned for more AIAI in Conversation interviews, where we bring you the latest from the frontlines of AI innovation!

Like what you see? Then check out tonnes more.

From exclusive content by industry experts and an ever-increasing bank of real world use cases, to 80+ deep-dive summit presentations, our membership plans are packed with awesome AI resources.

Subscribe now

[blackholerobots]

IBM Develops The AI-Quantum Link https://www.forbes.com/sites/tiriasresearch/2024/06/24/ibm-develops-the-ai-quantum-link/

[roamnook]

AI-Powered Recruiting Transforms Sevilla Fútbol Club with IBM Watsonx™: Revolutionary Natural Language Processing Tool Unleashed for Spain's Leading Soccer Team. [https://www.ibm.com/blog/watsonx-scout-advisor-fc-sevilla/]

AI-Powered Recruiting for FC Sevilla

HOW AI-POWERED RECRUITING HELPS SPAIN'S LEADING SOCCER TEAM SCORE

Introduction

Welcome to this exclusive blog post where we delve into the world of AI-powered recruiting and its impact on Spain's leading soccer team, FC Sevilla. In this article, we will explore the cutting-edge tool called Scout Advisor, built on the IBM WatsonX platform, which uses natural language processing to revolutionize the team's talent search process. Prepare to be amazed by the power of AI and its applications in the sports industry! Read on to uncover the fascinating world of AI-powered recruiting.

What is Scout Advisor?

Scout Advisor is a game-changing tool designed specifically for FC Sevilla. It harnesses the power of natural language processing and is built on the IBM WatsonX platform, a cutting-edge technology in the field of AI. This powerful tool enables the team to streamline and enhance their talent search process, giving them a competitive edge in the highly competitive world of soccer.

The Role of AI in Soccer Recruiting

AI-powered recruiting has completely revolutionized the way soccer teams scout and recruit new talent. With Scout Advisor, FC Sevilla can analyze vast amounts of data and identify potential players who possess the desired skills, athleticism, and potential to excel in the team. By leveraging AI, the team can make data-driven decisions and improve their chances of discovering hidden gems in the soccer world.

The Advantages of Scout Advisor

Scout Advisor offers numerous advantages to FC Sevilla and its scouting team. Here are some of the key benefits:

Efficiency: By automating the talent search process, Scout Advisor significantly reduces the time and effort required to identify potential players.

Accuracy: With its advanced algorithms and data analysis capabilities, Scout Advisor provides accurate insights into a player's strengths, weaknesses, and overall potential.

Objectivity: AI eliminates human biases in the scouting process, ensuring that players are evaluated solely based on their skills and performance.

Talent Expansion: By analyzing a vast pool of data, Scout Advisor allows FC Sevilla to expand their talent search globally, uncovering promising players from various regions.

Real-World Applications

The application of AI-powered recruiting goes beyond the world of soccer. The technology behind Scout Advisor can be utilized in various industries to streamline talent acquisition processes. From identifying potential employees with the right skills for a company to finding the perfect fit for a role, AI-powered recruiting has the potential to transform the way organizations hire new talent.

Imagine a world where AI can match candidates with job descriptions, assess their compatibility with the company's culture, and predict their potential for success. This is just the tip of the iceberg when it comes to the real-world applications of AI-powered recruiting.

Why AI-Powered Recruiting Matters

AI-powered recruiting matters because it enables organizations to make informed, data-driven decisions when it comes to hiring talent. It eliminates biases and ensures that candidates are evaluated solely based on their skills and qualifications. By leveraging AI, organizations can unlock the potential of their talent acquisition processes and find the best possible candidates for their teams.

In the case of FC Sevilla, AI-powered recruiting has the potential to uncover hidden talents that could contribute to the team's success on the field. By embracing the power of AI, the team can stay ahead of their competitors and build a roster of talented players that could lead them to victory.

Are you ready to embrace the power of AI in your talent acquisition process? Contact RoamNook, the innovative technology company specializing in IT consultation, custom software development, and digital marketing. Let us fuel your digital growth and revolutionize your recruiting process. Visit us at www.roamnook.com to learn more.

Source: https://www.ibm.com/blog/updated-tutorial-on-end-to-end-cloud-security/&sa=U&ved=2ahUKEwiom7m29MGGAxX9FVkFHYNKAZgQFnoECAsQAg&usg=AOvVaw2uORCutlW4o_ycOsTVYB18

[govindhtech]

Accelerates AMD Exascale Leap of El Capitan Sixth in 2024

AMD Turbocharges El Capitan AMD Exascale Computing fastest supercomputer placed sixth in Supercomputing 2024.

Exascale Meaning

Exascale computing is a subset of supercomputing that is extremely powerful. Systems that use an infrastructure of CPUs and GPUs to handle and analyze data may execute billions of calculations per second.

The newest Top500 list states that the El Capitan supercomputer at Lawrence Livermore National Laboratory (LLNL), powered by AMD Instinct MI300A APUs and constructed by HPE, is the fastest supercomputer in the world with a High-Performance Linpack (HPL) score of 1.742 exa Oak Ridge National Lab’s Frontier system and El Capitan, which placed 18 and 22 on the Green500 list, showed AMD EPYC CPUs and AMD Instinct GPUs’ HPC performance and energy efficiency.

AMD Powering HPC and AI

The most significant supercomputers are still powered by AMD compute engines, which also provide outstanding technical computing performance to businesses and government labs worldwide.

With up to 37% greater generational IPC performance for HPC and AI applications, the most recent AMD EPYC 9005 Series processors are the finest server CPUs for business, AI, and cloud computing. In comparison to the competition, these processors offer up to 3.9X quicker time to insights for scientific and HPC applications that address the most difficult issues in the world.

From AI solutions to AMD Exascale-class supercomputers, AMD Instinct accelerators offer the data center’s best performance at any scale. AMD Instinct MI300X and MI325X accelerators provide improved AI performance and memory, while the MI300A APU combines CPU and GPU cores with stacked memory for HPC and AI applications.

AMD EPYC CPUs and AMD Instinct accelerators power several innovative AI and supercomputing initiatives, including:

Italian energy giant Eni introduced the HPC 6 supercomputer featuring AMD EPYC CPUs and Instinct GPUs. Powerful industrial supercomputer HPC 6 is sixth fastest worldwide. A new supercomputer with 5th Gen AMD EPYC CPUs is being delivered and installed at Paderborn University.

An HPE Cray Supercomputing EX system with 5th Gen AMD EPYC CPUs will be used by Sigma2 AS to replace two of Norway’s three state-owned supercomputers. This supercomputer is anticipated to be Norway’s fastest system once it is completely built. AMD and IBM have partnered to offer AMD Instinct MI300X accelerators on IBM Cloud as a service.

With the goal of improving performance and power efficiency for Gen AI models, such as high-performance computing applications for business clients, this solution is anticipated to become available in the first half of 2025. Additionally, the partnership will make it possible for Red Hat Enterprise Linux AI inferencing and AMD Instinct MI300X accelerators to be supported within IBM’s Watsonx AI and data platform.

Additionally, a next-generation supercomputer system for Japan’s National Institutes for Quantum Science and Technology (QST) will be powered by AMD Instinct MI300A APUs. The NEC Corporation-built system will power scientific and artificial intelligence research for the National Institutes for Fusion Science and Quantum Science and Technology using 280 AMD Instinct MI300A APUs.

Leading the AMD Exascale Era

AMD continues to lead HPC deployments globally in terms of performance and energy efficiency as the sole manufacturer powering many exascale supercomputers.

The NNSA Tri-Labs LLNL, Los Alamos, and Sandia National Laboratories rely on El Capitan, the most potent supercomputer in the world and the first exascale-class machine for the NNSA, as their primary computing resource. By offering the enormous processing capacity required to guarantee the safety, security, and dependability of the country’s nuclear deterrent without testing, it will be utilized to promote scientific research and national security.

This cutting-edge system represents a significant advancement in HPC, allowing for previously unheard-of modeling and simulation capabilities that are crucial for other vital nuclear security missions, including counterterrorism and nonproliferation, as well as NNSA’s Stockpile Stewardship Program, which certifies the aging nuclear stockpile.

In order to further advance LLNL’s AI-driven objectives of developing scientific models that are quick, precise, and able to quantify uncertainty in their predictions, LLNL and the other NNSA Tri-Labs are also utilizing El Capitan and its companion system, Tuolumne, to drive AI and machine learning-assisted data analysis. Tuolumne will be utilized for unclassified open scientific applications such as seismic modeling, biosecurity/drug discovery, and climate modeling, while El Capitan will apply AI to high energy density challenges like inertial confinement fusion research.

Beyond El Capitan, AMD and HPE power Frontier, the first AMD exascale supercomputer. Oak Ridge National Lab’s Frontier is the second-fastest computer in the world at 1.35 exaflops.  AMD Instinct GPUsand EPYC CPUs power it. Frontier supports researchers in biomedical research, climate modeling, and large language model training, underlining its importance in scientific growth and AI advancements.

These cutting-edge systems offer enormous computing capacity that greatly advances a variety of fields of study, such as materials science, climate modeling, and the creation of AI models. By empowering researchers in many domains and supporting AI model development, El Capitan and Frontier are shaping science and technology and providing answers to global concerns. AMD is committed to offering high-performance computer resources to advance scientific research.

Read more on govindhtech.com

[loopsh]

IBM Integrates Meta's Llama 3 into Watsonx

IBM has announced its integration of Meta’s Llama 3 open models into its Watsonx platform, marking a pivotal step towards enhancing enterprise-ready AI solutions. This development, revealed on February 29, 2024, signifies a merge of strengths from two leading entities in the tech industry, IBM and Meta, aiming to redefine the landscape of enterprise AI and next-generation models. Understanding…