#FoundationModels

[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.

Read more on govindhteh.com

[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/ 

[shireen46]

Difference between Generative AI, LLMs, and Foundation Models

In recent years, the field of artificial intelligence has witnessed remarkable advancements, particularly in the development of sophisticated language models that have transformed the way we interact with machines. Among the key players in this AI revolution are Generative AI, Large Language Models, and Foundation Models. While these terms are often used interchangeably, they have distinct characteristics and serve different purposes. In this article, we will delve into the differences between these three categories of AI models to provide a better understanding of their respective roles and capabilities.

Generative AI:

Generative AI refers to a class of artificial intelligence models that are capable of generating creative content, often in the form of text, images, or even audio. These models are designed to produce novel output that is not directly copied from the input data. Generative AI systems, like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), work by learning patterns and generating content from scratch based on those patterns. Some popular examples of Generative AI include:

GANs (Generative Adversarial Networks): GANs consist of two neural networks, a generator, and a discriminator, which work in opposition to produce realistic outputs. GANs have been used to create realistic images, deepfakes, and more.

VAEs (Variational Autoencoders): VAEs are used to generate data by learning the underlying structure and variability within a dataset. They have applications in image generation and text generation.

Recurrent Neural Networks (RNNs): RNNs are used for sequence-to-sequence tasks, making them suitable for text generation, language translation, and more.Generative AI is a broad field with applications across various domains, including art, entertainment, content generation, and even scientific research.

Key characteristics of Generative AI:

a. Creativity: Generative AI systems are designed to be creative and produce content that is not found in their training data. They can generate new ideas, artworks, or text that is unique and often unpredictable.

b. Variability: These models can produce a wide range of outputs, making them useful in creative tasks such as art, music, and storytelling.

c. Not language-focused: While Generative AI can work with text, it is not limited to language generation and can be used for various creative applications.

Large Language Models:

Large Language Models

GPT (Generative Pre-trained Transformer): The GPT series, such as GPT-2 and GPT-3, are renowned for their text generation capabilities. They are often used for tasks like language translation, content generation, and chatbots.

BERT (Bidirectional Encoder Representations from Transformers): BERT models are designed for natural language understanding and perform exceptionally well on tasks like sentiment analysis, question-answering, and more.

Large Language Models are versatile and can be fine-tuned for specific tasks, making them valuable tools in various applications, from chatbots and virtual assistants to content generation and text classification.

Key characteristics of Large Language Models:

a. Language-centric: Large Language Models are primarily designed for natural language processing tasks, making them exceptionally good at tasks like text completion, conversation, and text summarization.

b. Transfer learning: They leverage pre-training on massive text corpora to generalize to various language-related tasks, making them versatile and adaptable.

c. Not necessarily creative: While Large Language Models can generate text, their output is often limited to producing coherent and contextually relevant content. They are less focused on creative, novel generation compared to other Generative AI models.

Foundation Models:

Foundation Models are at the forefront of AI research and technology. These models are designed to serve as the basis for a wide range of AI applications, including both language-related tasks and other domains. They are often large-scale models, like GPT-3 or its successors, and serve as the foundation upon which specialized models can be built.

Some examples of Foundation Models include:

OpenAI's GPT-3: While GPT-3 is a Large Language Model, it can also be viewed as a Foundation Model because of its extensive knowledge base and versatility in various applications, beyond just text generation.

Google's T5 (Text-To-Text Transfer Transformer): T5 is a model that frames all NLP tasks as a text-to-text problem, making it highly adaptable to a wide range of tasks.

Foundation Models serve as a base upon which developers and researchers can build specialized AI applications. They provide a strong starting point for various domains, such as natural language processing, computer vision, and more.

Key characteristics of Foundation Models:

a. Versatility: Foundation Models are general-purpose and versatile, capable of being fine-tuned for specific applications. They can serve as the starting point for various AI tasks beyond language processing, such as image recognition and even medical diagnosis.

b. Scalability: These models are often extremely large, with millions or even billions of parameters, allowing them to capture vast amounts of knowledge and nuances.

c. Potential for creative tasks: While not their primary focus, Foundation Models can be used for creative tasks when fine-tuned and adapted, but their primary strength lies in their versatility and adaptability.

Conclusion

In summary, Generative AI, Large Language Models, and Foundation Models are distinct categories of artificial intelligence models, each with its own set of characteristics and applications. Generative AI is known for its creativity and versatility in generating content, while Large Language Models, like GPT-3, excel in natural language processing tasks. Foundation Models serve as the cornerstone for a wide array of AI applications, providing a versatile starting point for specialized models. Understanding the differences between these categories is crucial for leveraging their capabilities effectively and choosing the right model for specific tasks in the evolving landscape of artificial intelligence.

[hitechnectar-htn]

Hello #TechGeeks, 

Did you know #FoundationModels in #AI are #algorithms that train and develop with broader datasets to execute various functions? 

Moreover, foundation models are built on conventional deep learning and transfer learning algorithms. 

Hence, read more about the Foundation Models in AI, A new Trend and the Future. 

https://bit.ly/3x50V8Z

#ArtificialIntelligence #mlops #datascientist #machinelearning #DeepLearning

[usahbtips]

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[govindhtech]

Amazon Bedrock now offers AI21 Studio’s Jamba-Instruct

AI21 Studio’s Labs in Amazon Bedrock

Create dependable generative AI applications by utilising AI21 Labs foundation models.

AI21 Labs AWS Advantages

Designed with the enterprise in mind

Use specially designed models to power text generation, long document summarising, and question answering to solve essential business activities.

Selection of model dimensions

Choose between the Jurassic-2 Mid, Jurassic-2 Ultra, and Jamba-Instruct models according to the requirements for context length, complexity, and other factors.

Devoted assistance

With the professional advice of AI21 account executives and solution architects, move from prototype to production.

Become acquainted with AI21 Labs

AI21 Labs focuses on creating cutting-edge FMs and AI Systems that let businesses use generative AI in their operations. With the help of their models, AI21 Labs hopes to enable organisations to develop AI solutions that promote confident decision-making, unbridled creativity, and clear communication all of which are necessary for businesses to prosper in the artificial intelligence era.

Use cases

Banking operations

Produce term sheets that are already formatted and prepared for sharing with stakeholders, as well as summarise the most important information from lengthy and complex papers, such as market analyses and corporate reports.

Retails

Produce at scale product descriptions and marketing content that is optimised for conversions, all while adhering to the tone, length, and style of your brand.

Client assistance

Give clients prompt, intelligible answers to their questions based on information sheets, policies, and papers.

Information handling

Increase productivity by facilitating teams’ ability to quickly and easily extract well-reasoned replies using natural language from intricate documentation or policies.

Versions of the models

Jamba-Instruction

AI21’s Jamba model, a hybrid SSM-Transformer, has been fine-tuned for optimal performance and quality, making Jamba-Instruct a dependable commercial option.

Maximum tokens: 256K

Languages Spoken: English

Instruction following, text production, document summary, and question responding are supported use cases.

No fine-tuning is supported.

AI21 Jurassic-2 ultra

Jurassic-2 Ultra

The most potent model available for AI21 for difficult text production jobs requiring the best possible results. Maximum number of tokens: 8,192

Languages spoken: Dutch, English, Spanish, French, German, Portuguese, and Italian

Supported use cases include: answering questions, summarising, creating drafts, extracting complex material, and coming up with ideas for jobs using deductive reasoning.

No fine-tuning is supported.

Jurassic-2 Mid

The mid-sized model from AI21 is used for sophisticated text creation tasks that demand both price and quality. Maximum number of tokens: 8,192

Languages Spoken: English

Supported use cases include: answering questions, summarising, creating drafts, extracting complex material, and coming up with ideas for jobs using deductive reasoning.

No fine-tuning is supported.

Introducing Task-Specific and Jurassic-2 APIs

AI21 labs Jurassic

Announcing the release of Jurassic-2, the most recent iteration of AI21 Studio’s foundation models, which will revolutionise the area of artificial intelligence with its superior quality and additional features. Not only that, but AI21 Studio is also making available their task-specific APIs, which have superior plug-and-play reading and writing capabilities than those of AI21 Studio rivals.

The second generation of AI21 Studio foundation models, known as Jurassic-2 (or J2), has several new features and notable quality enhancements, such as zero-shot instruction-following, lower latency, and multi-language compatibility.

Developers may access industry-leading APIs for specialised reading and writing operations right out of the box with task-specific APIs.

For a closer look at each, continue reading.

AI21 Labs Jurassic 2

Jurassic-2

AI21 Studio is pleased to introduce their brand-new, cutting-edge Large Language Model family. Not only is J2 a complete upgrade over their previous generation models, Jurassic-1, but it also comes with additional features and capabilities that set it apart from the competition.

The Jurassic-2 family consists of instruction-tuned language models for Jumbo and Grande in addition to three different sized basic language models: Large, Grande, and Jumbo.Image credit to AWS

On Stanford’s Holistic Evaluation of Language Models (HELM), the industry standard for language models, Jurassic is already causing a stir. AI21 Labs evaluated J2 Jumbo using HELM’s official repository, and it currently ranks second (and is still rising). Not to mention, their Grande mid-sized model outperforms versions up to 30 times larger in size, allowing users to maximise production costs and speed without compromising quality.

In contrast to Jurassic-1, what’s new?

Higher calibre

Utilising state-of-the-art pre-training techniques and the most recent data (as of mid-2022), J2’s Jumbo model has achieved an 86.8% win-rate on HELM according to their internal assessments, firmly establishing it as a premier choice in the LLM arena.

Assign capabilities

The zero-shot instruction features of J2’s best-in-class models enable them to be guided using natural language without the need for examples. These features have been added to J2’s Grande and Jumbo models. As an illustration, consider this:

Multilingual Assistance

J2 is compatible with a number of non-English languages, such as Dutch, Spanish, French, German, Portuguese, and Italian.

Achievement

J2’s models can outperform AI21 Studio earlier models by up to 30% in terms of latency.

You may now access every Jurassic-2 model on AI21 Studio playground and API. Here are some pointers and strategies for utilising the new Instruct models to get you going.

‍Task-Despecificated APIs

With the release of the Wordtune API set, AI21 Labs is also pleased to announce the debut of AI21 Studio’s new line of Task-Specific APIs, which will allow developers to access the language models behind AI21 Studio wildly successful consumer-facing reading and writing apps.

What makes task-specific APIs necessary?

AI21 Studio’s General Large Language Models are extremely strong and have been successfully customised by many of their clients to power their applications. But AI21 Studio have also seen that a lot of users have a lot of recurring use cases.

AI21 Studio’s ready-made, best-in-class language processing solutions enable developers to bypass many of the necessary model training and fine-tuning phases by giving them access to task-specific APIs.

Cutting-edge AI is used by Wordtune and Wordtune Read to help users with writing and reading chores, all while saving time and enhancing efficiency. AI21 Studio is making the AI engine underlying this range of award-winning apps available to developers with the introduction of Wordtune API, enabling them to fully utilise Wordtune’s features and incorporate them into their own apps:

Reword texts to suit any length, tone, or meaning by using paraphrasing.

Condense long texts into digestible, bite-sized chunks by summarising them.

Grammar and typo correction in real time is possible with Grammatical Error Correction (GEC).

Text Improvements: Learn how to make your writing more clear, more fluid, and more vocabulary-rich.

Text Segmentation: Divide lengthy texts into paragraphs that are each focused on a different subject.

Read more on Govindhtech.com

[govindhtech]

Amazon Bedrock Studio: Accelerate generative AI development

AWS is pleased to present to the public today Amazon Bedrock Studio, a brand-new web-based generative artificial intelligence (generative AI) development environment. By offering a fast prototyping environment with essential Amazon Bedrock technologies like Knowledge Bases, Agents, and Guardrails, Amazon Bedrock Studio speeds up the creation of generative AI applications.

Summary

A brand-new SSO-enabled web interface called Amazon Bedrock Studio offers developers from all over an organisation the simplest way to work together on projects, experiment with large language models (LLMs) and other foundation models (FMs), and refine generative AI applications. It simplifies access to various Foundation Models (FMs) and developer tools in Bedrock and provides a fast prototyping environment. AWS administrators can set up one or more workspaces for their company in the AWS Management Console for Bedrock and allow individuals or groups to utilise the workspace in order to enable Bedrock Studio.

In only a few minutes, begin developing applications

Using their company credentials (SSO), developers at your firm can easily log in to the Amazon Bedrock Studio online experience and begin experimenting with Bedrock FMs and application development tools right away. Bedrock Studio provides developers with a safe haven away from the AWS Management Console in which to utilise Bedrock features like Knowledge Bases, Amazon Guardrails, and Agents.

Create flexible generative AI applications

With Amazon Bedrock Studio, developers can gradually improve the accuracy and relevance of their generative AI applications. To acquire more accurate responses from their app, developers can begin by choosing an FM that is appropriate for their use case and then iteratively enhance the prompts. Then, they can add APIs to obtain the most recent results and use their own data to ground the app to receive more pertinent responses. Bedrock Studio streamlines and reduces the complexity of app development by automatically deploying pertinent AWS services (such Knowledge Bases and Agents). Additionally, enterprise use cases benefit from a secure environment because data and apps are never removed from the assigned AWS account.

Work together on projects with ease

Teams may brainstorm, test, and improve their generative AI applications together in Amazon Bedrock Studio‘s collaborative development environment. In addition to creating projects and inviting peers, developers may also share apps and insights and receive immediate feedback on their prototypes. Access control is a feature of Bedrock Studio projects that guarantees that only members with permission can use the apps and resources inside of a project.

Encourage creativity without worrying about infrastructure management

Knowledge bases, agents, and guardrails are examples of managed resources that are automatically installed in an AWS account when developers construct applications in Amazon Bedrock Studio. Because these Bedrock resources are always available and scaleable as needed, they don’t need to worry about the underlying compute and storage infrastructure. Furthermore, the Bedrock API makes it simple to access these resources. This means that by utilising the Bedrock API, you can easily combine the generative AI apps created in Bedrock Studio with their workflows and processes.

Take precautions to ensure the finest answers

To make sure their programme doesn’t provide incorrect output, developers can install content filters and create guardrails for both user input and model replies. To acquire the desired results from their apps, they can add prohibited topics and configure filtering levels across different categories to customise the behaviour of Guardrail.

As a developer, you can now log into Bedrock Studio and begin experimenting with your company’s single sign-on credentials. Within Bedrock Studio, you may create apps with a variety of high-performing models, assess them, and distribute your generative AI creations. You can enhance a model’s replies by following the stages that the user interface walks you through. You can play around with the model’s settings, set limits, and safely integrate tools, APIs, and data sources used by your business. Working in teams, you can brainstorm, test, and improve your generative AI apps without needing access to the AWS Management Console or sophisticated machine learning (ML) knowledge.

You can be sure that developers will only be able to utilise the functionality offered by Bedrock Studio and won’t have wider access to AWS infrastructure and services as an Amazon Web Services (AWS) administrator.

Let me now walk you through the process of installing Amazon Bedrock Studio.

Use Amazon Bedrock Studio to get started

You must first create an Amazon Bedrock Studio workspace as an AWS administrator, after which you must choose and add the users you wish to grant access to the workspace. You can provide the relevant individuals with the workspace URL once it has been built. Users with the necessary permissions can start developing generative AI apps, create projects inside their workspace, and log in using single sign-on.

Establish a workstation in Amazon Bedrock Studio

Select Bedrock Studio from the bottom left pane of the Amazon Bedrock dashboard.

You must use the AWS IAM Identity Centre to set up and secure the single sign-on integration with your identity provider (IdP) before you can create a workspace. See the AWS IAM Identity Centre User Guide for comprehensive instructions on configuring other IdPs, such as Okta, Microsoft Entra ID, and AWS Directory Service for Microsoft Active Directory. You set up user access using the IAM Identity Centre default directory for this demo.

Next, select Create workspace, fill in the specifics of your workspace, and create any AWS Identity and Access Management (IAM) roles that are needed.

Additionally, you have the option to choose the workspace’s embedding models and default generative AI models. Select Create once you’re finished.

Choose the newly formed workspace next.

Next, pick the users you wish to grant access to this workspace by choosing User management and then Add users or groups.

You can now copy the Bedrock Studio URL and share it with your users from the Overview tab.

Create apps for generative AI using Amazon Bedrock Studio

Now that the Bedrock Studio URL has been provided, builders can access it and log in using their single sign-on login credentials. Here at Amazon Bedrock Studio, welcome! Allow me to demonstrate how to select among top-tier FMs, import your own data, use functions to call APIs, and use guardrails to secure your apps.

Select from a number of FMs that lead the industry

By selecting examine, you may begin choosing from among the FMs that are offered and use natural language prompts to examine the models.

If you select Build, you may begin developing generative AI applications in playground mode, play around with model settings, refine your application’s behaviour through iterative system prompts, and create new feature prototypes.

Bring your personal data

Using Bedrock Studio, you can choose from a knowledge base built in Amazon Bedrock or securely bring your own data to customise your application by supplying a single file.

Make API calls using functions to increase the relevancy of model responses

When replying to a prompt, the FM can dynamically access and incorporate external data or capabilities by using a function. The model uses an OpenAPI schema you supply to decide which function it needs to call.

A model can include data into its response through functions that it is not directly aware of or has access to beforehand. For instance, even though the model doesn’t save the current weather information, a function may enable the model to acquire it and incorporate it into its response.

Using Guardrails for Amazon Bedrock, secure your apps

By putting in place safeguards tailored to your use cases and responsible AI rules, you may build boundaries to encourage safe interactions between users and your generative AI apps.

The relevant managed resources, including knowledge bases, agents, and guardrails, are automatically deployed in your AWS account when you construct apps in Amazon Bedrock Studio. To access those resources in downstream applications, use the Amazon Bedrock API.

Amazon Bedrock Studio availability

The public preview of Amazon Bedrock Studio is now accessible in the AWS Regions US West (Oregon) and US East (Northern Virginia).

Read more on govindhtech.com

[govindhtech]

Innovations in Generative AI and Foundation Models

Generative AI in Therapeutic Antibody Development: With the cooperation announced today, Boehringer Ingelheim and IBM will be able to employ IBM’s foundation model technology to find new candidate antibodies for the development of effective treatments.

Boehringer Ingelheim’s Andrew Nixon, Global Head of Biotherapeutics Discovery, said, “We are very excited to collaborate with the research team at IBM, who share our vision of making in silico biologic drug discovery a reality.” “We will create an unparalleled platform for expedited antibody discovery by collaborating with IBM scientists, and I am sure that this will allow Boehringer to create and provide novel treatments for patients with significant unmet needs.”

Boehringer plans to use a pre-trained AI model created by IBM, which will be further refined using additional proprietary data owned by Boehringer. Vice President of Accelerated Discovery at IBM Research Alessandro Curioni stated, “IBM has been at the forefront of creating generative AI models that extend AI’s impact beyond the domain of language.” “We are excited to now enable Boehringer, a pioneer in the creation and production of antibody-based treatments, to leverage IBM’s multimodal foundation model technologies to help quicken Boehringer’s ability to develop new therapeutics.”

Foundational models for the finding of antibodies

Therapeutic antibodies play a key role in the management of numerous illnesses, such as infectious, autoimmune, and cancerous conditions. The identification and creation of therapeutic antibodies encompassing a variety of epitopes continues to be an extremely difficult and time-consuming procedure, even with significant technological advancements.

Researchers from IBM and Boehringer will work together to use in-silico techniques to speed up the antibody discovery process.  New human antibody sequences will be generated in silico using the sequence, structure, and molecular profile data of disease-relevant targets as well as success criteria for therapeutically relevant antibody molecules, such as developability, specificity, and affinity. The efficacy and speed of antibody discovery, as well as the quality of anticipated antibody candidates, are intended to be enhanced by these techniques, which are based on new IBM foundation model technology.

The defined targets are designed with antibody candidates using IBM’s foundation model technologies, which have proven effective in producing biologics and small molecules with relevant target affinities. AI-enhanced simulation is then used to screen the antibody candidates and select and refine the best binders for the target. The antibody candidates will be produced at mini-scales and evaluated experimentally by Boehringer Ingelheim as part of a validation process. Subsequently, the outcomes of the lab trials will be applied to enhance the in-silico techniques through feedback loops.

Boehringer is creating a cutting-edge digital ecosystem to facilitate the acceleration of medication discovery and development and to generate new breakthrough prospects to improve the lives of patients by working with top academic and industry partners.

Generative AI in Therapeutic Antibody Development

Additionally, IBM is using foundation models and Generative AI to speed up the discovery and development of new biologics and small chemicals, and this study is the latest in this endeavor. Earlier in the year, the business’s Generative AI model accurately predicted the physico-chemical characteristics of tiny compounds that resembled drugs. 

Pre-trained models for drug-target interactions and protein-protein interactions are developed using a variety of heterogeneous, publically available data sets by the IBM Biomedical Foundation Model Technologies.  In order to provide newly created proteins and small molecules with the required qualities, the pre-trained models are subsequently refined using particular confidential data belonging to IBM’s partner.

Concerning Boehringer Ingelheim

Innovative treatments that change lives now and for future generations are being developed by Boehringer Ingelheim. As a top biopharmaceutical business focused on research, it adds value through innovation in highly unmet medical needs. Having been family-owned since its founding in 1885, Boehringer Ingelheim adopts a long-term, sustainable viewpoint. The two business groups, Human Pharma and Animal Health, employ more than 53,000 people to service more than 130 markets. Go to www.boehringer-ingelheim.com to learn more.

Regarding IBM

IBM is a top global supplier of Generative AI, hybrid cloud, and consulting services. They assist clients in over 175 countries to acquire a competitive advantage in their respective industries, optimize business processes, cut expenses, and capitalize on insights from their data. IBM’s hybrid cloud platform and Red Hat OpenShift are used by over 4,000 government and business institutions in critical infrastructure domains including financial services, telecommunications, and healthcare to facilitate their digital transformations in a timely, secure, and effective manner.

IBM clients are given open and flexible alternatives by IBM’s ground-breaking advances in artificial intelligence (AI), quantum computing, industry-specific cloud solutions, and consultancy. IBM has a strong history of upholding integrity, openness, accountability, diversity, and customer service.

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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/ 

[govindhtech]

Amazon SageMaker HyperPod Presents Amazon EKS Support

Amazon SageMaker HyperPod

Cut the training duration of foundation models by up to 40% and scale effectively across over a thousand AI accelerators.

We are happy to inform you today that Amazon SageMaker HyperPod, a specially designed infrastructure with robustness at its core, will enable Amazon Elastic Kubernetes Service (EKS) for foundation model (FM) development. With this new feature, users can use EKS to orchestrate HyperPod clusters, combining the strength of Kubernetes with the robust environment of Amazon SageMaker HyperPod, which is ideal for training big models. By effectively scaling across over a thousand artificial intelligence (AI) accelerators, Amazon SageMaker HyperPod can save up to 40% of training time.

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SageMaker HyperPod: What is it?

The undifferentiated heavy lifting associated with developing and refining machine learning (ML) infrastructure is eliminated by Amazon SageMaker HyperPod. Workloads can be executed in parallel for better model performance because it is pre-configured with SageMaker’s distributed training libraries, which automatically divide training workloads over more than a thousand AI accelerators. SageMaker HyperPod occasionally saves checkpoints to guarantee your FM training continues uninterrupted.

You no longer need to actively oversee this process because it automatically recognizes hardware failure when it occurs, fixes or replaces the problematic instance, and continues training from the most recent checkpoint that was saved. Up to 40% less training time is required thanks to the robust environment, which enables you to train models in a distributed context without interruption for weeks or months at a time. The high degree of customization offered by SageMaker HyperPod enables you to share compute capacity amongst various workloads, from large-scale training to inference, and to run and scale FM tasks effectively.

Advantages of the Amazon SageMaker HyperPod

Distributed training with a focus on efficiency for big training clusters

Because Amazon SageMaker HyperPod comes preconfigured with Amazon SageMaker distributed training libraries, you can expand training workloads more effectively by automatically dividing your models and training datasets across AWS cluster instances.

Optimum use of the cluster’s memory, processing power, and networking infrastructure

Using two strategies, data parallelism and model parallelism, Amazon SageMaker distributed training library optimizes your training task for AWS network architecture and cluster topology. Model parallelism divides models that are too big to fit on one GPU into smaller pieces, which are then divided among several GPUs for training. To increase training speed, data parallelism divides huge datasets into smaller ones for concurrent training.

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Robust training environment with no disruptions

You can train FMs continuously for months on end with SageMaker HyperPod because it automatically detects, diagnoses, and recovers from problems, creating a more resilient training environment.

Customers may now use a Kubernetes-based interface to manage their clusters using Amazon SageMaker HyperPod. This connection makes it possible to switch between Slurm and Amazon EKS with ease in order to optimize different workloads, including as inference, experimentation, training, and fine-tuning. Comprehensive monitoring capabilities are provided by the CloudWatch Observability EKS add-on, which offers insights into low-level node metrics on a single dashboard, including CPU, network, disk, and other. This improved observability includes data on container-specific use, node-level metrics, pod-level performance, and resource utilization for the entire cluster, which makes troubleshooting and optimization more effective.

Since its launch at re:Invent 2023, Amazon SageMaker HyperPod has established itself as the go-to option for businesses and startups using AI to effectively train and implement large-scale models. The distributed training libraries from SageMaker, which include Model Parallel and Data Parallel software optimizations to assist cut training time by up to 20%, are compatible with it. With SageMaker HyperPod, data scientists may train models for weeks or months at a time without interruption since it automatically identifies, fixes, or replaces malfunctioning instances. This frees up data scientists to concentrate on developing models instead of overseeing infrastructure.

Because of its scalability and abundance of open-source tooling, Kubernetes has gained popularity for machine learning (ML) workloads. These benefits are leveraged in the integration of Amazon EKS with Amazon SageMaker HyperPod. When developing applications including those needed for generative AI use cases organizations frequently rely on Kubernetes because it enables the reuse of capabilities across environments while adhering to compliance and governance norms. Customers may now scale and maximize resource utilization across over a thousand AI accelerators thanks to today’s news. This flexibility improves the workflows for FM training and inference, containerized app management, and developers.

With comprehensive health checks, automated node recovery, and work auto-resume features, Amazon EKS support in Amazon SageMaker HyperPod fortifies resilience and guarantees continuous training for big-ticket and/or protracted jobs. Although clients can use their own CLI tools, the optional HyperPod CLI, built for Kubernetes settings, can streamline job administration. Advanced observability is made possible by integration with Amazon CloudWatch Container Insights, which offers more in-depth information on the health, utilization, and performance of clusters. Furthermore, data scientists can automate machine learning operations with platforms like Kubeflow. A reliable solution for experiment monitoring and model maintenance is offered by the integration, which also incorporates Amazon SageMaker managed MLflow.

In summary, the HyperPod service fully manages the HyperPod service-generated Amazon SageMaker HyperPod cluster, eliminating the need for undifferentiated heavy lifting in the process of constructing and optimizing machine learning infrastructure. This cluster is built by the cloud admin via the HyperPod cluster API. These HyperPod nodes are orchestrated by Amazon EKS in a manner akin to that of Slurm, giving users a recognizable Kubernetes-based administrator experience.

Important information

The following are some essential details regarding Amazon EKS support in the Amazon SageMaker HyperPod:

Resilient Environment: With comprehensive health checks, automated node recovery, and work auto-resume, this integration offers a more resilient training environment. With SageMaker HyperPod, you may train foundation models continuously for weeks or months at a time without interruption since it automatically finds, diagnoses, and fixes errors. This can result in a 40% reduction in training time.

Improved GPU Observability: Your containerized apps and microservices can benefit from comprehensive metrics and logs from Amazon CloudWatch Container Insights. This makes it possible to monitor cluster health and performance in great detail.

Scientist-Friendly Tool: This release includes interaction with SageMaker Managed MLflow for experiment tracking, a customized HyperPod CLI for job management, Kubeflow Training Operators for distributed training, and Kueue for scheduling. Additionally, it is compatible with the distributed training libraries offered by SageMaker, which offer data parallel and model parallel optimizations to drastically cut down on training time. Large model training is made effective and continuous by these libraries and auto-resumption of jobs.

Flexible Resource Utilization: This integration improves the scalability of FM workloads and the developer experience. Computational resources can be effectively shared by data scientists for both training and inference operations. You can use your own tools for job submission, queuing, and monitoring, and you can use your current Amazon EKS clusters or build new ones and tie them to HyperPod compute.

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