#Watsonx.data

[govindhtech]

Watsonx.data Presto C++ With Intel Sapphire Rapids On AWS

Using Watsonx.data Presto C++ with the Intel Sapphire Rapid Processor on AWS to speed up query performance

Over the past 25 years, there have been notable improvements in database speed due to IBM and Intel’s long-standing cooperation. The most recent generation of Intel Xeon Scalable processors, when paired with Intel software, can potentially improve IBM Watsonx.data performance, according to internal research conducted by IBM.

A hybrid, managed data lake house, IBM Watsonx.data is tailored for workloads including data, analytics, and artificial intelligence. Using engines like Presto and Spark to drive corporate analytics is one of the highlights. Watsonx.data also offers a single view of your data across hybrid cloud environments and a customizable approach.

Presto C++

The next edition of Presto, called Presto C++, was released by IBM in June. It was created by open-source community members from Meta, IBM, Uber, and other companies. The Velox, an open-source C++ native acceleration library made to be compatible with various compute engines, was used in the development of this query engine in partnership with Intel. In order to further improve query performance through efficient query rewrite, IBM also accompanied the release of the Presto C++ engine with a query optimizer built on decades of experience.

Summary

A C++ drop-in replacement for Presto workers built on the Velox library, Presto C++ is also known as the development name Prestissimo. It uses the Proxygen C++ HTTP framework to implement the same RESTful endpoints as Java workers. Presto C++ does not use JNI and does not require a JVM on worker nodes because it exclusively uses REST endpoints for communication with the Java coordinator and amongst workers.

Inspiration and Goals

Presto wants to be the best data lake system available. The native Java-based version of the Presto evaluation engine is being replaced by a new C++ implementation using Velox in order to accomplish this goal.

In order to allow the Presto community to concentrate on more features and improved connectivity with table formats and other data warehousing systems, the evaluation engine has been moved to a library.

Accepted Use Cases

The Presto C++ evaluation engine supports just certain connectors.

Reads and writes via the Hive connection, including CTAS, are supported.

Only reads are supported for iceberg tables.

Both V1 and V2 tables, including tables with delete files, are supported by the Iceberg connector.

TPCH.naming=standard catalog property for the TPCH connector.

Features of Presto C++

Task management: Users can monitor and manage tasks using the HTTP endpoints included in Presto C++. This tool facilitates tracking ongoing procedures and improves operational oversight.

Data processing across a network of nodes can be made more effective by enabling the execution of functions on distant nodes, which improves scalability and distributed processing capabilities.

For secure internal communication between nodes, authentication makes use of JSON Web Tokens (JWT), guaranteeing that data is safe and impenetrable while being transmitted.

Asynchronous data caching with prefetching capabilities is implemented. By anticipating data demands and caching it beforehand, this maximizes processing speed and data retrieval.

Performance Tuning: Provides a range of session parameters, such as compression and spill threshold adjustments, for performance tuning. This guarantees optimal performance of data processing operations by enabling users to adjust performance parameters in accordance with their unique requirements.

Limitations of Presto C++

There are some drawbacks to the C++ evaluation engine:

Not every built-in function is available in C++. A query failure occurs when an attempt is made to use unimplemented functions. See Function Coverage for a list of supported functions.

C++ does not implement all built-in types. A query failure will occur if unimplemented types are attempted to be used.

With the exception of CHAR, TIME, and TIME WITH TIMEZONE, all basic and structured types in Data Types are supported. VARCHAR, TIMESTAMP, and TIMESTAMP WITH TIMEZONE are subsumptions of these.

The length n in varchar[n] is not honored by Presto C++; it only supports the limitless length VARCHAR.

IPADDRESS, IPPREFIX, UUID, KHYPERLOGLOG, P4HYPERLOGLOG, QDIGEST, TDIGEST, GEOMETRY, and BINGTILE are among the types that are not supported.

The C++ evaluation engine does not use all of the plugin SPI. Specifically, several plugin types are either fully or partially unsupported, and C++ workers will not load any plugins from the plugins directory.

The C++ evaluation engine does not support PageSourceProvider, RecordSetProvider, or PageSinkProvider.

Block encodings, parametric types, functions, and types specified by the user are not supported.

At the split level, the event listener plugin is not functional.

See Remote Function Execution for information on how user-defined functions differ from one another.

The C++ evaluation engine has a distinct memory management system. Specifically:

There is no support for the OOM killer.

There is no support for the reserved pool.

Generally speaking, queries may utilize more memory than memory arbitration permits. Refer to Memory Management.

Functions

reduce_agg

Reduce_agg is not allowed to return null in the inputFunction or the combineFunction of C++-based Presto. This is acceptable but ill-defined behavior in Presto (Java). See reduce_agg for more details about reduce_agg in Presto.

Amazon Elastic Compute Cloud (EC2) R7iz instances are high-performance CPU instances that are designed for memory. With a sustained all-core turbo frequency of 3.9 GHz, they are the fastest 4th Generation Intel Xeon Scalable-based (Sapphire Rapids) instances available in the cloud. R7iz instances can lower the total cost of ownership (TCO) and provide performance improvements of up to 20% over Z1d instances of the preceding generation. They come with integrated accelerators such as Intel Advanced Matrix Extensions (Intel AMX), which provide a much-needed substitute for clients with increasing demands for AI workloads.

R7iz instances are well-suited for front-end Electronic Design Automation (EDA), relational database workloads with high per-core licensing prices, and workloads including financial, actuarial, and data analytics simulations due to their high CPU performance and large memory footprint.

IBM and Intel have collaborated extensively to offer open-source software optimizations to Watsonx.data, Presto, and Presto C++. In addition to the hardware enhancements, Intel 4th Gen Xeon has produced positive Watsonx.data outcomes.

Based on publicly available 100TB TPC-DS Query benchmarks, IBM Watsonx.data with Presto C++ v0.286 and query optimizer on AWS ROSA, running on Intel processors (4th generation), demonstrated superior price performance over Databrick’s Photon engine, with better query runtime at comparable cost.

Read more on Govindhtech.com

[sonalikrishnan]

watsonx.data: Scale AI Workloads, for all your data, anywhere 

Data is fundamental to every business, fueling applications, predictive insights, and improved experiences. However, fully harnessing data’s potential is often hindered by limitations in storage and access for analytics and AI. 

AI begins with how your data is stored, managed, and governed to ensure it is reliable and scalable. watsonx.data, leveraging a data lakehouse architecture, can help you cut your data warehouse costs by up to 50% and streamline access to governed data for AI. 

IBM watsonx.data is an open, hybrid, and governed data store designed to optimize all data, analytics, and AI workloads. 

watsonx.data enables enterprises to seamlessly expand their analytics and AI capabilities by leveraging a purpose-built data store. This data store is built on an open lakehouse architecture, incorporating robust querying, governance, and open data formats to facilitate efficient data access and sharing. By utilizing watsonx.data, you can establish connections to data sources in a matter of minutes, swiftly obtain reliable insights, and significantly reduce costs associated with traditional data warehousing. 

With watsonx.data, users can access their increasing amount of data through a single point of entry while applying the multiple fit for purpose query engines to unlock the valuable insights. 

Know more about watsonx.data: https://pragmaedge.com/watsonx-data/ 

Find answers to your questions about Watsonx.data: https://pragmaedge.com/watsonx-data-faqs/ 

[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

[jcmarchi]

Wipro and IBM collaborate to propel enterprise AI

New Post has been published on https://thedigitalinsider.com/wipro-and-ibm-collaborate-to-propel-enterprise-ai/

Wipro and IBM collaborate to propel enterprise AI

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In a bid to accelerate the adoption of AI in the enterprise sector, Wipro has unveiled its latest offering that leverages the capabilities of IBM’s watsonx AI and data platform.

The extended partnership between Wipro and IBM combines the former’s extensive industry expertise with IBM’s leading AI innovations. The collaboration seeks to develop joint solutions that facilitate the implementation of robust, reliable, and enterprise-ready AI solutions.

The Wipro Enterprise AI-Ready Platform harnesses various components of the IBM watsonx suite, including watsonx.ai, watsonx.data, and watsonx.governance, alongside AI assistants. It offers clients a comprehensive suite of tools, large language models (LLMs), streamlined processes, and robust governance mechanisms, laying a solid foundation for the development of future industry-specific analytic solutions.

Jo Debecker, Managing Partner & Global Head of Wipro FullStride Cloud, said: “This expanded partnership with IBM combines our deep contextual cloud, AI, and industry expertise with IBM’s leading AI innovation capabilities.”

A key aspect of this collaboration is the establishment of the IBM TechHub@Wipro, a centralised tech hub aimed at supporting joint client pursuits. This initiative will bring together subject matter experts, engineers, assets, and processes to drive and support AI initiatives.

Kate Woolley, General Manager of IBM Ecosystem, commented: “We’re pleased to reach this new milestone in our 20-year partnership to support clients through the combination of Wipro’s and IBM’s joint expertise and technology, including watsonx.”

The Wipro Enterprise AI-Ready Platform offers infrastructure and core software for AI and generative AI workloads, enhancing automation, dynamic resource management, and operational efficiency in the enterprise. Moreover, it caters to specialised industry use cases, such as banking, retail, health, energy, and manufacturing, offering tailored solutions for customer support, marketing, feedback analysis, and more.

Nagendra Bandaru, Managing Partner and President of Wipro Enterprise Futuring, highlighted the flexibility of the platform, stating: “Wipro’s Enterprise AI-Ready Platform will allow clients to easily integrate and standardise multiple data sources augmenting AI- and GenAI-enabled transformation across business functions.”

In addition to facilitating AI governance through the AI lifecycle, the platform prioritises responsible AI practices, ensuring transparency, data protection, and compliance with relevant laws and regulations.

As part of this collaboration, Wipro associates will undergo training in IBM hybrid cloud, AI, and data analytics technologies, further enhancing their capabilities in developing joint solutions.

(Photo by Carson Masterson on Unsplash)

See also: Reddit is reportedly selling data for AI training

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 BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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

Tags: ai, artificial intelligence, enterprise, ibm, ibm watson, platform, watsonx, wipro

[ericvanderburg]

IBM watsonx.data Helps Investigators Cut Paperwork Time and Boost Officer Productivity

http://securitytc.com/T163nD

[johnbrownnn]

Newsletter Sed ut perspiciatis unde. Subscribe IBM Consulting, IBM’s professional services branch, and Amazon Web Services have added new generative AI solutions to three services for contact centers and the supply chain, the companies announced on Oct. 18. The purpose of the expanded relationship between IBM Consulting and AWS is “to help more mutual clients operationalize and derive value from generative artificial intelligence,” IBM Consulting stated in a press release. In addition, IBM Consulting intends to provide exclusive training to 10,000 consultants and host the watsonx.data storage solution as software-as-a-service on AWS. Jump to: Generative AI for summarization, IT Ops and more comes to AWS contact center and supply chain services More must-read AI coverage Joint IBM Consulting and AWS services that have been enhanced with generative AI capabilities include: Contact Center Modernization with Amazon Connect. Platform Services on AWS. Supply Chain Ensemble on AWS. SEE: AWS committed $100 million to a Generative AI Innovation Center in June 2023. (TechRepublic)  Contact Center Modernization with Amazon Connect provides generative AI, which can summarize and categorize voice or text interactions. These functions are designed to enable hand-offs between a chatbot and a live agent; the agent will receive summarized details of the customer interaction to speed up resolution times. For details on pricing and international availability, go to this AWS page for Amazon Connect. Platform Services, which AWS introduced in November 2022, will now include generative AI for IT Ops, automation and platform engineering. The generative AI applies to observability techniques and intelligent issue resolution. Lastly, Supply Chain Ensemble on AWS will introduce a virtual assistant for supply chain professionals. It is designed to optimize inventories, reduce costs, streamline logistics and assess risks. Pricing for AWS depends on which services are included. A price calculator can be found here. All of the above offerings are available throughout AWS’s Availability Zones. IBM Consulting plans to add generative AI for coding and reverse engineering to the IBM Consulting Cloud Accelerator. This accelerator is available globally. Competitors to Amazon Connect and AWS supply chain Competitors to AWS’s supply chain services include Microsoft Dynamics 365 Supply Chain Management, SAP Supply Chain Management and Oracle Fusion Cloud SCM. Competitors to Amazon Connect for contact center services include Talkdesk, Twilio Flex and Webex Contact Center. IBM plans to train 10,000 consultants on AWS generative AI services IBM Consulting plans to give its partners training on AWS generative AI services, including top use cases and best practices for client engagement. The end result, IBM proposes, will be 10,000 consultants trained and skilled in generative AI by 2024. “Enterprise clients are looking for expert help [consultants] to build a strategy and develop generative AI use cases that can drive business value and transformation – while mitigating risks,” said Manish Goyal, senior partner, global AI and analytics leader at IBM Consulting, in a press release. “In talking with clients daily, we’re seeing major demand from enterprises to help think through how they can best drive value using generative AI both rapidly and responsibly,” Goyal said in an email to TechRepublic. “We know this is critical to build strategies that are informed, enhancing our ability to review end to end workflows and find opportunities where generative AI automation might be able to complement traditional AI for innovation, to cite one example,” Goyal said. Watsonx.data added to AWS Watsonx.data, a data storage platform, is now available on AWS as a fully-managed SaaS solution accessible through AWS Marketplace. This is one of the results of a 2022 agreement between AWS and IBM to more closely link their services. Watsonx.ai, a platform for training generative AI models, and watsonx.governance, which assists with AI governance, are expected to follow watsonx.data by 2024. Source link

[arielmcorg]

#IA - IBM disponibiliza mundialmente Watsonx, su plataforma de IA Generativa

Está disponible mundialmente IBM Watsonx, plataforma de datos e IA Generativa lista para empresas. Está nueva apuesta de la compañía había sido anunciada en IBM Think, el mayo pasado, con tres componentes (Fuente IBM Argentina): watsonx.ai studio para nuevos modelos fundacionales, IA generativa y aprendizaje automático (ahora disponible) El almacén watsonx.data, que proporciona soluciones…

[govindhtech]

IBM Watsonx.data: Transforming Data Flexibility & Efficiency

In addition to Spark, Presto, and Presto C++, Watsonx.data provides a selection of open query engines that are perfect for a wide range of applications.

Businesses will face more difficulties in handling their expanding data as the worldwide data storage market is predicted to more than treble by 2032. The adoption of hybrid cloud solutions is revolutionising data management, improving adaptability, and elevating overall organisational performance.

Businesses can build flexible, high-performing data ecosystems that are ready for AI innovation and future growth by concentrating on five essential components of cloud adoption for optimising data management, from changing data strategy to guaranteeing compliance.

The development of data management techniques

With generative AI, data management is changing drastically. Companies are increasingly using hybrid cloud solutions, which mix private and public cloud benefits. These solutions are especially helpful for data-intensive industries and businesses implementing AI strategies to drive expansion.

Companies want to put 60% of their systems in the cloud by 2025, according to a McKinsey & Company report, highlighting the significance of adaptable cloud strategy. In order to counter this trend, hybrid cloud solutions provide open designs that combine scalability and excellent performance. Working with systems that can adjust to changing requirements without sacrificing performance or security is what this change means for technical workers.

Workload portability and smooth deployment

The ability to quickly deploy across any cloud or on-premises environment is one of the main benefits of hybrid cloud solutions. Workload portability made possible by cutting-edge technologies like Red Hat OpenShift further increases this flexibility.

With this feature, enterprises can match their infrastructure to hybrid and multicloud cloud data strategies, guaranteeing that workloads may be scaled or transferred as needed without being restricted to a single environment. For businesses to deal with changing business needs and a range of regulatory standards, this flexibility is essential.

Improving analytics and AI with unified data access

The advancement of AI and analytics capabilities is being facilitated by hybrid cloud infrastructures. According to a Gartner report from 2023, “two out of three enterprises use hybrid cloud to power their AI initiatives,” highlighting the platform’s crucial place in contemporary data strategy. These solutions offer uniform data access through the use of open standards, facilitating the easy sharing of data throughout an organisation without the need for significant migration or restructuring.

Moreover, cutting-edge programs like IBM Watsonx.data use vector databases like Milvus, an open-source program that makes it possible to store and retrieve high-dimensional vectors quickly. For AI and machine learning activities, especially in domains like computer vision and natural learning processing, this integration is vital. It increases the relevance and accuracy of AI models by giving access to a larger pool of reliable data, spurring innovation in these fields.

These characteristics enable more effective data preparation for AI models and applications, which benefits data scientists and engineers by improving the accuracy and applicability of AI-driven insights and predictions.

Using appropriate query engines to maximize performance

The varied nature of data workloads in the field of data management necessitates a flexible query processing strategy. Watsonx.data offers a variety of open query engines that are suitable for various applications, including Spark, Presto, and Presto C++. It also provides integration options for data warehouse engines, such as Db2 and Netezza. Data teams are able to select the best tool for each work thanks to this flexibility, which improves efficiency and lowers costs.

For example, Spark is great at handling complicated, distributed data processing jobs, while Presto C++ may be used for high-performance, low-latency queries on big datasets. Compatibility with current workflows and systems is ensured through interaction with well-known data warehouse engines.

In contemporary enterprises, this adaptability is especially useful when handling a variety of data formats and volumes. Watsonx.data solves the difficulties of quickly spreading data across several settings by enabling enterprises to optimise their data workloads.

In a hybrid world: compliance and data governance

Hybrid cloud architectures provide major benefits in upholding compliance and strong data governance in the face of ever more stringent data requirements. In comparison to employing several different cloud services, hybrid cloud solutions can help businesses manage cybersecurity, data governance, and business continuity more successfully, according to a report by FINRA (Financial Industry Regulatory Authority).

Hybrid cloud solutions enable enterprises to use public cloud resources for less sensitive workloads while keeping sensitive data on premises or in private clouds, in contrast to pure multicloud configurations that can make compliance efforts across different providers more difficult. With integrated data governance features like strong access control and a single point of entry, IBM Watsonx.data improves this strategy. This method covers a range of deployment criteria and constraints, which facilitates the implementation of uniform governance principles and enables compliance with industry-specific regulatory requirements without sacrificing security.

Adopting hybrid cloud for data management that is ready for the future

Enterprise data management has seen a substantial change with the development of hybrid cloud solutions. Solutions such as IBM Watsonx.data, which provide a harmony of flexibility, performance, and control, are helping companies to create more inventive, resilient, and efficient data ecosystems.

Enterprise data and analytics will be shaped in large part by the use of hybrid cloud techniques as data management continues to change. Businesses may use Watsonx.data‘s sophisticated capabilities to fully use their data in hybrid contexts and prepare for the adoption of artificial intelligence in the future. This allows them to negotiate this shift with confidence.

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[govindhtech]

IBM Db2 AI Updates: Smarter, Faster, Better Database Tools

IBM Db2

Designed to handle mission-critical workloads worldwide.

What is IBM Db2?

IBM Db2 is a cloud-native database designed to support AI applications at scale, real-time analytics, and low-latency transactions. It offers database managers, corporate architects, and developers a single engine that is based on decades of innovation in data security, governance, scalability, and availability.

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When moving to hybrid deployments, create the next generation of mission-critical apps that are available 24/7 and have no downtime across all clouds.

Support for all contemporary data formats, workloads, and programming languages will streamline development.

Support for open formats, including Apache Iceberg, allows teams to safely communicate data and information, facilitating quicker decision-making.

Utilize IBM Watsonx integration for generative artificial intelligence (AI) and integrated machine learning (ML) capabilities to implement AI at scale.

Use cases

Power next-gen AI assistants

Provide scalable, safe, and accessible data so that developers may create AI-powered assistants and apps.

Build new cloud-native apps for your business

Create cloud-native applications with low latency transactions, flexible scalability, high concurrency, and security that work on any cloud. Amazon Relational Database Service (RDS) now offers it.

Modernize mission-critical web and mobile apps

Utilize Db2 like-for-like compatibility in the cloud to modernize your vital apps for hybrid cloud deployments. Currently accessible via Amazon RDS.

Power real-time operational analytics and insights

Run in-memory processing, in-database analytics, business intelligence, and dashboards in real-time while continuously ingesting data.

Data sharing

With support for Apache Iceberg open table format, governance, and lineage, you can share and access all AI data from a single point of entry.

In-database machine learning

With SQL, Python, and R, you can create, train, assess, and implement machine learning models from inside the database engine without ever transferring your data.

Built for all your workloads

IBM Db2 Database

Db2 is the database designed to handle transactions of any size or complexity. Currently accessible via Amazon RDS.

IBM Db2 Warehouse

You can safely and economically conduct mission-critical analytical workloads on all kinds of data with IBM Db2 Warehouse. Watsonx.data integration allows you to grow AI workloads anywhere.

IBM Db2 Big SQL

IBM Db2 Big SQL is a high-performance, massively parallel SQL engine with sophisticated multimodal and multicloud features that lets you query data across Hadoop and cloud data lakes.

Deployment options

You require an on-premises, hybrid, or cloud database. Use Db2 to create a centralized business data platform that operates anywhere.

Cloud-managed service

Install Db2 on Amazon Web Services (AWS) and IBM Cloud as a fully managed service with SLA support, including RDS. Benefit from the cloud’s consumption-based charging, on-demand scalability, and ongoing improvements.

Cloud-managed container

Launch Db2 as a cloud container:integrated Db2 into your cloud solution and managed Red Hat OpenShift or Kubernetes services on AWS and Microsoft Azure.

Self-managed infrastructure or IaaS

Take control of your Db2 deployment by installing it as a conventional configuration on top of cloud-based infrastructure-as-a-service or on-premises infrastructure.

IBM Db2 Updates With AI-Powered Database Helper

Enterprise data is developing at an astonishing rate, and companies are having to deal with ever-more complicated data environments. Their database systems are under more strain than ever as a result of this. Version 12.1 of IBM’s renowned Db2 database, which is scheduled for general availability this week, attempts to address these demands. The latest version redefines database administration by embracing AI capabilities and building on Db2’s lengthy heritage.

The difficulties encountered by database administrators who must maintain performance, security, and uptime while managing massive (and quickly expanding) data quantities are covered in Db2 12.1. A crucial component of their strategy is IBM Watsonx’s generative AI-driven Database Assistant, which offers real-time monitoring, intelligent troubleshooting, and immediate replies.

Introducing The AI-Powered Database Assistant

By fixing problems instantly and averting interruptions, the new Database Assistant is intended to minimize downtime. Even for complicated queries, DBAs may communicate with the system in normal language to get prompt responses without consulting manuals.

The Database Assistant serves as a virtual coach in addition to its troubleshooting skills, speeding up DBA onboarding by offering solutions customized for each Db2 instance. This lowers training expenses and time. By enabling DBAs to address problems promptly and proactively, the database assistant should free them up to concentrate on strategic initiatives that improve the productivity and competitiveness of the company.

IBM Db2 Community Edition

Now available

Db2 12.1

No costs. No adware or credit card. Simply download a single, fully functional Db2 Community License, which you are free to use for as long as you wish.

What you can do when you download Db2

Install on a desktop or laptop and use almost anywhere. Join an active user community to discover events, code samples, and education, and test prototypes in a real-world setting by deploying them in a data center.

Limits of the Community License

Community license restrictions include an 8 GB memory limit and a 4 core constraint.

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[govindhtech]

IBM Watsonx.data Offers VSCode, DBT & Airflow Dataops Tools

We are happy to inform that VSCode, Apache Airflow, and data-build-tool a potent set of tools for the contemporary dataops stack are now supported by IBM watsonx.data. IBM Watsonx.data delivers a new set of rich capabilities, including data build tool (dbt) compatibility for both Spark and Presto engines, automated orchestration with Apache Airflow, and an integrated development environment via VSCode. These functionalities enable teams to effectively construct, oversee, and coordinate data pipelines.

The difficulty with intricate data pipelines

Building and maintaining complicated data pipelines that depend on several engines and environments is a challenge that organizations must now overcome. Teams must continuously move between different languages and tools, which slows down development and adds complexity.

It can be challenging to coordinate workflows across many platforms, which can result in inefficiencies and bottlenecks. Data delivery slows down in the absence of a smooth orchestration tool, which postpones important decision-making.

A coordinated strategy

Organizations want a unified, efficient solution that manages process orchestration and data transformations in order to meet these issues. Through the implementation of an automated orchestration tool and a single, standardized language for transformations, teams can streamline their workflows, facilitating communication and lowering the difficulty of pipeline maintenance. Here’s where Apache Airflow and DBT come into play.

Teams no longer need to learn more complicated languages like PySpark or Scala because dbt makes it possible to develop modular structured query language (SQL) code for data transformations. The majority of data teams are already familiar with SQL, thus database technology makes it easier to create, manage, and update transformations over time.

Throughout the pipeline, Apache Airflow automates and schedules jobs to minimize manual labor and lower mistake rates. When combined, dbt and Airflow offer a strong framework for easier and more effective management of complicated data pipelines.

Utilizing IBM watsonx.data to tie everything together

Although strong solutions like Apache Airflow and DBT are available, managing a developing data ecosystem calls for more than just a single tool. IBM Watsonx.data adds the scalability, security, and dependability of an enterprise-grade platform to the advantages of these tools. Through the integration of VSCode, Airflow, and DBT within watsonx.data, it has developed a comprehensive solution that makes complex data pipeline management easier:

By making data transformations with SQL simpler, dbt assists teams in avoiding the intricacy of less used languages.

By automating orchestration, Airflow streamlines processes and gets rid of bottlenecks.

VSCode offers developers a comfortable environment that improves teamwork and efficiency.

This combination makes pipeline management easier, freeing your teams to concentrate on what matters most: achieving tangible business results. IBM Watsonx.data‘s integrated solutions enable teams to maintain agility while optimizing data procedures.

Data Build Tool’s Spark adaptor

The data build tool (dbt) adapter dbt-watsonx-spark is intended to link Apache Spark with dbt Core. This adaptor facilitates Spark data model development, testing, and documentation.

FAQs

What is data build tool?

A transformation workflow called dbt enables you to complete more tasks with greater quality. Dbt can help you centralize and modularize your analytics code while giving your data team the kind of checks and balances that are usually seen in software engineering workflows. Before securely delivering data models to production with monitoring and visibility, work together on them, version them, test them, and record your queries.

DBT allows you and your team to work together on a single source of truth for metrics, insights, and business definitions by compiling and running your analytics code against your data platform. Having a single source of truth and the ability to create tests for your data helps to minimize errors when logic shifts and notify you when problems occur.

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[govindhtech]

IBM Watsonx.governance Removes Gen AI Adoption Obstacles

The IBM Watsonx platform, which consists of Watsonx.ai, Watsonx.data, and Watsonx.governance, removes obstacles to the implementation of generative AI.

Complex data environments, a shortage of AI-skilled workers, and AI governance frameworks that consider all compliance requirements put businesses at risk as they explore generative AI’s potential.

Generative AI requires even more specific abilities, such as managing massive, diverse data sets and navigating ethical concerns due to its unpredictable results.

IBM is well-positioned to assist companies in addressing these issues because of its vast expertise using AI at scale. The IBM Watsonx AI and data platform provides solutions that increase the accessibility and actionability of AI while facilitating data access and delivering built-in governance, thereby addressing skills, data, and compliance challenges. With the combination, businesses may fully utilize AI to accomplish their goals.

Forrester Research’s The Forrester Wave: AI/ML Platforms, Q3, 2024, by Mike Gualtieri and Rowan Curran, published on August 29, 2024, is happy to inform that IBM has been rated as a strong performer.

IBM is said to provide a “one-stop AI platform that can run in any cloud” by the Forrester Report. Three key competencies enable IBM Watsonx to fulfill its goal of becoming a one-stop shop for AI platforms: Using Watsonx.ai, models, including foundation models, may be trained and used. To store, process, and manage AI data, use watsonx.data. To oversee and keep an eye on all AI activity, use watsonx.governance.

Watsonx.ai

Watsonx.ai: a pragmatic method for bridging the AI skills gap

The lack of qualified personnel is a significant obstacle to AI adoption, as indicated by IBM’s 2024 “Global AI Adoption Index,” where 33% of businesses cite this as their top concern. Developing and implementing AI models calls both certain technical expertise as well as the appropriate resources, which many firms find difficult to come by. By combining generative AI with conventional machine learning, IBM Watsonx.ai aims to solve these problems. It consists of runtimes, models, tools, and APIs that make developing and implementing AI systems easier and more scalable.

Let’s say a mid-sized retailer wants to use demand forecasting powered by artificial intelligence. Creating, training, and deploying machine learning (ML) models would often require putting together a team of data scientists, which is an expensive and time-consuming procedure. The reference customers questioned for The Forrester Wave AI/ML Platforms, Q3 2024 report said that even enterprises with low AI knowledge can quickly construct and refine models with watsonx.ai’s “easy-to-use tools for generative AI development and model training .”

For creating, honing, and optimizing both generative and conventional AI/ML models and applications, IBM Watsonx.ai offers a wealth of resources. To train a model for a specific purpose, AI developers can enhance the performance of pre-trained foundation models (FM) by fine-tuning parameters efficiently through the Tuning Studio. Prompt Lab, a UI-based tools environment offered by Watsonx.ai, makes use of prompt engineering strategies and conversational engagements with FMs.

Because of this, it’s simple for AI developers to test many models and learn which one fits the data the best or what needs more fine tuning. The watsonx.ai AutoAI tool, which uses automated machine learning (ML) training to evaluate a data set and apply algorithms, transformations, and parameter settings to produce the best prediction models, is another tool available to model makers.

It is their belief that the acknowledgement from Forrester further confirms IBM’s unique strategy for providing enterprise-grade foundation models, assisting customers in expediting the integration of generative AI into their operational processes while reducing the risks associated with foundation models.

The watsonx.ai AI studio considerably accelerates AI deployment to suit business demands with its collection of pre-trained, open-source, and bespoke foundation models from third parties, in addition to its own flagship Granite series. Watsonx.ai makes AI more approachable and indispensable to business operations by offering these potent tools that help companies close the skills gap in AI and expedite their AI initiatives.

Watsonx.data

Real-world methods for addressing data complexity using Watsonx.data

As per 25% of enterprises, data complexity continues to be a significant hindrance for businesses attempting to utilize artificial intelligence. It can be extremely daunting to deal with the daily amount of data generated, particularly when it is dispersed throughout several systems and formats. These problems are addressed by IBM Watsonx.Data, an open, hybrid, and controlled data store that is suitable for its intended use.

Its open data lakehouse architecture centralizes data preparation and access, enabling tasks related to artificial intelligence and analytics. Consider, for one, a multinational manufacturing corporation whose data is dispersed among several regional offices. Teams would have to put in weeks of work only to prepare this data manually in order to consolidate it for AI purposes.

By providing a uniform platform that makes data from multiple sources more accessible and controllable, Watsonx.data can help to simplify this. To make the process of consuming data easier, the Watsonx platform also has more than 60 data connections. The software automatically displays summary statistics and frequency when viewed data assets. This makes it easier to quickly understand the content of the datasets and frees up a business to concentrate on developing its predictive maintenance models, for example, rather than becoming bogged down in data manipulation.

Additionally, IBM has observed via a number of client engagement projects that organizations can reduce the cost of data processing by utilizing Watsonx.data‘s workload optimization, which increases the affordability of AI initiatives.

In the end, AI solutions are only as good as the underlying data. A comprehensive data flow or pipeline can be created by combining the broad capabilities of the Watsonx platform for data intake, transformation, and annotation. For example, the platform’s pipeline editor makes it possible to orchestrate operations from data intake to model training and deployment in an easy-to-use manner.

As a result, the data scientists who create the data applications and the ModelOps engineers who implement them in real-world settings work together more frequently. Watsonx can assist enterprises in managing their complex data environments and reducing data silos, while also gaining useful insights from their data projects and AI initiatives. Watsonx does this by providing comprehensive data management and preparation capabilities.

Watsonx.Governance

Using Watsonx.Governance to address ethical issues: fostering openness to establish trust

With ethical concerns ranking as a top obstacle for 23% of firms, these issues have become a significant hurdle as AI becomes more integrated into company operations. In industries like finance and healthcare, where AI decisions can have far-reaching effects, fundamental concerns like bias, model drift, and regulatory compliance are particularly important. With its systematic approach to transparent and accountable management of AI models, IBM Watsonx.governance aims to address these issues.

The organization can automate tasks like identifying bias and drift, doing what-if scenario studies, automatically capturing metadata at every step, and using real-time HAP/PII filters by using watsonx.governance to monitor and document its AI model landscape. This supports organizations’ long-term ethical performance.

By incorporating these specifications into legally binding policies, Watsonx.governance also assists companies in staying ahead of regulatory developments, including the upcoming EU AI Act. By doing this, risks are reduced and enterprise trust among stakeholders, including consumers and regulators, is strengthened. Organizations can facilitate the responsible use of AI and explainability across various AI platforms and contexts by offering tools that improve accountability and transparency. These tools may include creating and automating workflows to operationalize best practices AI governance.

Watsonx.governance also assists enterprises in directly addressing ethical issues, guaranteeing that their AI models are trustworthy and compliant at every phase of the AI lifecycle.

IBM’s dedication to preparing businesses for the future through seamless AI integration

IBM’s AI strategy is based on the real-world requirements of business operations. IBM offers a “one-stop AI platform” that helps companies grow their AI activities across hybrid cloud environments, as noted by Forrester in their research. IBM offers the tools necessary to successfully integrate AI into key business processes. Watsonx.ai empowers developers and model builders to support the creation of AI applications, while Watsonx.data streamlines data management. Watsonx.governance manages, monitors, and governs AI applications and models.

As generative AI develops, businesses require partners that are fully versed in both the technology and the difficulties it poses. IBM has demonstrated its commitment to open-source principles through its design, as evidenced by the release of a family of essential Granite Code, Time Series, Language, and GeoSpatial models under a permissive Apache 2.0 license on Hugging Face. This move allowed for widespread and unrestricted commercial use.

Watsonx is helping IBM create a future where AI improves routine business operations and results, not just helping people accept AI.

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[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/ 

[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.

[govindhtech]

IBM Watsonx Assistant improves responsible AI for your firm

This explains why sports, where even a small competitive edge can mean the difference between first and second place, have seen such a high number of early adopters of AI.

Consider the US Open from last year, where IBM Watsonx predicted each player’s degree of advantage or disadvantage in the singles draw. Sevilla FC introduced a watsonx-based technology abroad to give scouts thorough data-driven identification and assessment of possible signings. Additionally, EDGE3 will integrate with IBM Watsonx to enhance the college programme and player decision-making process, assisting colleges and athletes in navigating the ever-complex world of recruiting.

The quick adoption of these examples in enterprises goes hand in hand. For example, in order to help important industries, they are collaborating with Adobe to integrate Watsonx into their platform. IBM team is collaborating with SAP to augment SAP products with more artificial intelligence, machine learning, and other intelligent technologies that can increase business outcomes for our mutual clients.AI efforts are being launched by companies of all sizes and sectors.

Giving Responsible AI Priority:

Governance: Watsonx.governance provides resources for risk management, transparency, and staying ahead of AI-related policies in the future. This contains features that guarantee exploitability and track machine learning models.

Data management: Watsonx.data supports ethical and safe data management. It guarantees that data is used in accordance with regulatory rules and offers a dedicated data repository that grows with your needs.

Openness: Because the platform is based on open-source technologies, AI models can be customized and expanded upon. This transparency encourages teamwork and helps prevent the prejudices that come with closed systems.

Features and Specifications:

Multi-model Variety: For particular jobs, you can integrate your own bespoke models or select from pre-built IBM models or open-source models.

AutoAI: This tool streamlines workflows for professionals and makes AI development more approachable for novices by automating tasks like model construction, data preparation, and hyperparameter tweaking.

AI Assistants: Tasks can be automated and AI implemented in a variety of business areas, such as customer service or HR, using pre-built applications powered by Watsonx. There is no need to create code in order to deploy these aids.

Flexibility: Watsonx can be deployed on a range of infrastructures, including on-premises servers, private clouds, and public clouds, to meet a variety of business requirements.

Watsonx hopes to enable companies to properly develop and implement AI by providing these characteristics, emphasising compliance, fairness, and transparency.

It’s crucial to remember that responsible AI implementation calls for more than simply a platform. To guarantee the ethical development and application of AI, businesses must have defined policies and procedures in place.

They ought to be posing the following two queries:

Is AI a good fit for my company?

Indeed. Every organization, whether large or little, is likely in a competitive industry where artificial intelligence can make a major difference. Before using AI, decide on the use case and goals.

In order to enhance their current technology, partners from a variety of industries are already incorporating IBM Watsonx into their processes, products, and solutions in novel ways. For instance, clients can leverage IBM AI with proprietary data stored in Box to speed up business operations by combining Watsonx with Box’s Content Cloud.

Quantum Street AI

Quantum Street AI is a financial investment and wealth management company that provides a comprehensive AI platform with Watsonx integration for professional investors. The platform covers 50,000 publicly traded worldwide firms and asset classes and represents USD 6 billion in client assets.

IBM’s strategy, which employs smaller models tailored to particular business use cases, also assists in lowering the barrier to entry for AI by assisting businesses in adopting particular models that operate on less expensive infrastructure and provide greater flexibility in terms of deployment on-premises, in private cloud, or on the public cloud.

How can I guarantee ethical AI innovation for my business?

Corporate executives of today should be considering how to use AI ethically rather than whether or not to investigate it for their company. There is no margin for mistake when implementing AI in the workplace; the proper safeguards must be put in place.

Governance is an essential component of responsible AI, and it need to be the primary focus of every developer, software supplier, or company planning to use AI. For this reason, Watsonx. Governance’s release was such a big deal for IBM. They give businesses a toolkit to help them embrace transparency, manage risk, and prepare for future regulations centered on artificial intelligence.

Watsonx.data

IBM Watsonx.data

Watsonx.data is a purpose-built data store that partners may use to use their data. It allows businesses to expand AI workloads for all of their data, regardless of where it is stored. IBM’s long-standing stance that such data belongs to the partner is a significant differentiator for IBM. As an alternative, the Granite model series (with publicly available data sources) is available on watsonx.ai, which is used by numerous partners.

IBM partners take the usage of data seriously, just like they do. Consider their partnership with Dun & Bradstreet, which combines Watsonx with the Dun & Bradstreet Data Cloud to assist businesses in expanding their usage of generative AI in an ethical manner.

IBM Watsonx Assistant is being used by Boxes, another IBM Partner, to assist retailers in obtaining buyer-ready data and customer insights so they may introduce new products and provide samples to customers. Additionally, Watsonx Foundation models are being used by partners like to address some of the other difficult aspects of AI, such hallucinations.

IBM Watsonx Assistant is being used by Boxes, another IBM Partner, to assist retailers in obtaining buyer-ready data and customer insights so they may introduce new products and provide samples to customers. Additionally, Watsonx Foundation models are being used by partners like to address some of the other difficult aspects of AI, such hallucinations.

IBM Watsonx Assistant is being used by Boxes, another IBM Partner, to assist retailers in obtaining buyer-ready data and customer insights so they may introduce new products and provide samples to customers. Additionally, Watsonx Foundation models are being used by partners like to address some of the other difficult aspects of AI, such hallucinations.

IBM Watsonx Assistant is being used by Boxes, another IBM Partner, to assist retailers in obtaining buyer-ready data and customer insights so they may introduce new products and provide samples to customers. Additionally, Watsonx Foundation models are being used by partners like to address some of the other difficult aspects of AI, such hallucinations.

IBM Watsonx Assistant is being used by Boxes, another IBM Partner, to assist retailers in obtaining buyer-ready data and customer insights so they may introduce new products and provide samples to customers. Additionally, Watsonx Foundation models are being used by partners like to address some of the other difficult aspects of AI, such hallucinations.

It’s wonderful to say that IBM uses its many years of experience leading the industry, making investments, and doing AI research to support partners at every stage of their AI journey. Regardless of your company’s age, size, form, or sector, IBM offers the appropriate partner programmed and technologies to support the adoption and growth of responsible AI in its enterprise.

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[govindhtech]

IBM Watsonx AI Power empowers Amazon re:Invent 2023!

Amazon re:Invent will include IBM Watsonx a data and AI system, safety features, and adaptive AI advice services

“By 2026, more than 80% of enterprises will have deployed GenAI-enabled applications in production environments and/or used generative AI models or APIs, up from less than 5% in 2023,” a Gartner analysis states. They must, however, have the flexibility to operate it on their current cloud systems if they are to be successful.

For this reason, They are committed to growing the partnership between IBM and AWS, giving customers the freedom to design and manage their artificial intelligence initiatives using the IBM Watsonx AI and data platform and AWS AI assistants.

These AI initiatives are supported by vast amounts of data, therefore businesses are turning more and more to data lakehouses to consolidate this data into a single location that they can access, clean, and manage. In light of this, Red Hat OpenShift and Red Hat OpenShift Services on AWS (ROSA), both of which are accessible via the AWS Marketplace, currently provide fully managed software-as-a-service (SaaS) on watsonx.data, a functional data store based on an open data lakehouse architecture.

Early in 2024, the watsonx.ai new generation studio for AI makers and the watsonx.governance toolset will be released, bringing the whole watsonx platform to AWS. With the help of reliable data, speed, and governance, customers can now train, fine-tune, and implement AI models with more flexibility, enabling them to operate their AI processes wherever they happen to be.

IBM Watsonx will demonstrate at AWS ReInvent how customers using AWS Sagemaker to access Llama 2 may leverage the watsonx.governance tools to control the AI and training data so that it can run transparently and safely as it grows. Additionally, Watsonx.governance may assist in managing these models in accordance with legal requirements and hazards related to the model and the application that uses it.

In addition, they will be revealing a number of noteworthy developments about our rapidly expanding collaboration and displaying the following collaborative innovations:

The Managed Security Service Providers (MSSPs) and Cloud System Integrators may now implement IBM security products on AWS more quickly thanks to a new initiative from IBM Security designed specifically for service providers. With a focus on safeguarding SMB customers, this program assists security companies in creating and delivering threat detection and data protection services. Additionally, by using IBM Security software, which has built-in AWS connections that greatly speed up and simplify onboarding, service providers may provide services that can be advertised in the AWS Marketplace.

Teams have been working on integrating Apptio Cloudability, a cloud cost-management solution, with IBM Watsonx Turbonomic, an IT resource management tool, enabling continuous hybrid cloud optimization since IBM’s purchase of Apptio completed in August. Currently, the Cloudability interface allows for the visualization of important Turbonomic optimization parameters, enabling more in-depth cost analysis and cost reductions for Amazon Cloud setups.

Workload Modernization: They provide deployment and support tools and services to streamline and automate the process of migrating IBM Planning Analytics, Db2 Warehouse, and IBM Maximo Application Suite from on-premise to as-a-service versions on Amazon Web Services.

Expanding Software Portfolio: As of right now, they have 25 SaaS products on AWS, including IBM Watsonx Turbonomic, APP Connect, watsonx.data, Maximo Application Suite, and three new Guardium Insights SaaS versions. The Amazon marketplace now has over 70 IBM offerings. As part of our partnership’s continuous worldwide growth, our customers in Denmark, France, Germany, and the UK may now purchase IBM software and SaaS via the AWS Marketplace. The catalog is now available in restricted availability.

Besides software, IBM Watsonx is expanding its generative AI skills and knowledge with AWS to provide innovative solutions to customers and instruct thousands of consultants on AWS generative AI services. Additionally, IBM opened an Innovation Lab at the IBM Client Experience Center in Bangalore in partnership with AWS. By doing this, IBM is expanding on its current proficiency with AWS generative AI services, such as Amazon SageMaker, Amazon CodeWhisperer, and Amazon Bedrock.

IBM is the only IT business with complementary technologies covering data and AI, automation, security, and sustainability capabilities, all built on Red Hat Open Shift Service on AWS and running cloud-native on AWS, in addition to consulting skills particular to AWS.

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[govindhtech]

IBM and AWS Expand Partnership to Deal Generative AI Solutions

IBM and Amazon Web Service(AWS) announced a partnership to enable more clients operationalize and benefit from generative AI. IBM Consulting plans to train 10,000 consultants on AWS by 2024 to deepen and expand its generative AI expertise and deliver joint solutions and services with generative AI capabilities to help clients across critical use cases.

IBM Consulting and AWS offer AI solutions and services to clients in many industries. Now, the firms are adding generative AI to their products and services to help clients swiftly integrate AI into AWS-based business and IT operations. IBM Consulting and AWS will launch these solutions:

Contact Center Modernization with Amazon Connect IBM Consulting and AWS used generative AI to create summarization and categorization functions for voice and digital interactions to allow chatbot-live agent transfers and provide summarized details to speed resolution and improve quality management.

AWS Platform Services This product, launched in November 2022, now uses generative AI to handle IT Ops, automation, and platform engineering across the cloud value chain. Through intelligent issue resolution and observability, the new generative AI capabilities help clients improve business serviceability and availability for AWS-hosted apps. Uptime and mean time repair will improve, allowing clients to respond promptly to concerns.

Supply Chain Ensemble on AWS This anticipated service will include a virtual assistant to help supply chain workers meet customer expectations, improve inventories, decrease costs, streamline logistics, and identify supply chain risks.

IBM Consulting will also incorporate AWS generative AI technologies into its own IBM Consulting Cloud Accelerator to speed up cloud transformation for AWS clients. This aids reverse engineering, code development, and conversion.

Dedication to AWS watsonx integration skills and growth

IBM is one of the first AWS Partners to use Amazon Bedrock, a fully managed service that makes industry-leading foundation models (FMs) available through an API so clients can choose the best model for their use case. IBM has extensive experience with AWS’s generative AI services, including Amazon SageMaker and Amazon CodeWhisperer.

Clients implementing generative AI need AI knowledge and a strong grasp of AWS capabilities. IBM Consulting’s Center of Excellence for Generative AI provides mutual clients with access to generative AI experts.

Today, IBM Consulting announced it will train 10,000 consultants on AWS generative AI services by 2024. An exclusive, partner-only program will train them on the top use cases and best practices for customer engagement with AWS generative AI services. This will improve their understanding, let them interact with technical personnel, and support AWS-innovating clients.

“Enterprise clients want expert help to build a strategy and develop generative AI use cases that drive business value and transformation while mitigating risks,” said IBM Consulting Senior Partner, Global AI & Analytics Leader Manish Goyal. “Paired with IBM’s AI heritage and deep expertise in business transformation on AWS, this suite of reengineered solutions with embedded generative AI capabilities can help our mutual clients to scale generative AI applications rapidly and responsibly on their platform of choice.”

IBM is also fulfilling client demand for generative AI capabilities on AWS by offering watsonx.data, a fit-for-purpose data store built on an open lakehouse architecture, as a fully managed SaaS option in AWS Marketplace. Watsonx.ai and watsonx.governance will be on AWS by 2024. This adds on the two businesses’ past commitments to simplify IBM data, AI, and security product consumption on AWS.

“Our customers are increasingly seeking technical support and AI expertise to build and implement a generative AI strategy that drives business value from their entire cloud value chain,” said AWS Managing Director, Global Systems Integrators Chris Niederman. “We are excited to be working with IBM to include embedded generative AI capabilities that assist our mutual customers scale their applications and help IBM consultants deepen their expertise on best practices for customer engagement with AWS generative AI services.”

IBM is also fulfilling client demand for generative AI capabilities on AWS by offering watsonx.data, a fit-for-purpose data store built on an open lakehouse architecture, as a fully managed SaaS option in AWS Marketplace. Watsonx.ai and watsonx.governance will be on AWS by 2024. This adds on the two businesses’ past commitments to simplify IBM data, AI, and security product consumption on AWS.

Scalable generative AI for telecom

IBM and AWS’s strong partnership benefits clients. Bouygues Telecom, a leading French communications service provider with a history of innovation, hired IBM Consulting to support its evolving cloud strategy to explore, design, and implement AI use cases at scale while allowing teams to choose cloud and AI providers based on departmental and application needs.

The team used IBM Garage to co-design a custom data and AI reference architecture for Bouygues Telecom’s cloud and on-premises AI and data projects across numerous cloud scenarios.

IBM Consulting helped Bouygues Telecom construct proof-of-concept models and scale them into production fast with the new AWS AI platform, reducing costs and risks. The platform lets their data scientists focus on complicated, high-value AI projects rather than single solutions, improving productivity, purpose, and satisfaction.

A changing relationship

IBM and AWS have over 40 years of combined AI solution experience and work with clients who want to use AI for cost, efficiency, and growth either by demonstrating the technology, defining use cases, or co-creating bespoke solutions.

AWS Premier Tier Services Partner IBM has over 22,000 AWS certifications and 17 AWS Service Delivery and 16 AWS Competency designations. Today’s news strengthens this historic collaboration and shared enterprise AI benefit. IBM Consulting helps clients set guardrails that correspond with the organization’s principles and norms, minimize bias, and manage data security, lineage, and provenance to expand business AI.

IBM Consulting facilitates business transformation for clients with hybrid cloud and AI technologies and our open ecosystem of partners. Many of the world’s most innovative and important firms trust us to update and secure their most complex systems due to our deep industry expertise in strategy, experience design, technology, and operations. Open collaboration and IBM Garage, our co-creation strategy, help our 160,000 consultants turn ideas into results.

Future direction and intent statements by IBM are subject to change or withdrawal without notice and are goals and objectives only.

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