Beyond the AI pilot project: Building a foundation for generative AI

In previous articles, we talked about developing concrete AI use cases for your organization and how to kick off your first AI pilot project. The next step? Evolving your artificial intelligence (AI) experiment from a science project into a full-fledged production AI application.

There are a lot of factors to consider in this process, but it's important to remember that all this AI stuff is still software. The skills and disciplines you and your teams have developed over the years will all come to bear in this new era of AI, with a few new factors thrown in to keep things exciting.

It's also important to remember that the strengths that open source brings to software development also apply to AI. At Red Hat we believe that open source is critical to the future of AI for reasons of speed, safety, security, inclusion, democratization, flexibility and more.

With that in mind, here we've collected 11 resources that can help you build a solid foundation for your existing and future generative AI (gen AI) applications and experiments.

1. Get started with AI for enterprise: A beginner’s guide

Whether you’re just getting started with your AI journey, looking to understand more about the impact AI will have on your business or figuring out how to scale your existing AI implementations, this e-book aims to answer many of the questions you may have about AI today.

In it you'll discover:

• The differences between predictive AI and gen AI
• A variety of concrete use cases for AI in your organization
• How to choose the right AI model for different applications
• Why model tuning can often be more effective (and efficient!) than model building
• A step-by-step methodology for getting started with AI
• How Red Hat can help you along the way

Download the e-book

2. Top considerations for building a foundation for generative AI

We all recognize that gen AI is enabling new forms of innovation and optimization, reshaping human-machine collaboration and inspiring new approaches to problem solving and knowledge generation. Of course, as with all new technologies, there are emerging concerns that need to be understood and addressed, including issues related to data privacy, data ownership, biases, explainability and more.

This e-book discusses many of these topics, and includes chapters about:

• Exploring new possibilities for business innovation
• Considerations for building a foundation for gen AI
• Innovating more quickly with a flexible open foundation

It also outlines some of the things you should look for in an AI platform to help mitigate potential issues and to simplify AI adoption and development.

Download the e-book

3. Why open source is critical to the future of AI

At Red Hat we believe that since everyone can benefit from AI, everyone should also be able to contribute to its development and direction. AI shouldn't be owned and controlled exclusively by a small number of powerful corporations—it needs to be open, accessible and democratized.

To help make this vision a reality, we are applying our decades of open source experience to the development of AI tools and frameworks that will allow everyone to contribute to—and benefit from—AI while also helping shape its future and evolution. In this article we go over the many reasons why we believe open source is absolutely fundamental to the future of AI.

Read the full article on the blog

4. Open source AI for developers

Discover how open source and AI work hand in hand to help developers to build, test and deploy faster and more efficient AI-infused applications.

In this e-book you'll explore Red Hat OpenShift AI from a developer's perspective, including essential open source tools like Jupyter Notebooks, PyTorch and advanced monitoring and observability features. MLOps, CI/CD workflows, and building generative and predictive AI applications are also covered, followed by some real-world AI use cases.

Chapters include:

• Open source and AI: A transformative combination
• Plan your development journey
• Build innovative AI-based applications
• Adopt advanced tools and technologies for AI
• Ready, set, develop: Start building AI-enabled applications

Download the e-book

5. Simplify AI adoption for faster time to value

While it's increasingly easy for organizations to start experimenting with the power of AI, turning those experiments into business value remains challenging, and the separation between AI teams, developers and IT can create additional delays. Success demands treating AI development as a continuous process, seamlessly integrating machine learning operations (MLOps) with existing developer operations (DevOps) practices.

Learn how Red Hat can help your organization achieve its AI goals with Red Hat AI -- our modular, composable AI platform for building, training, tuning, serving, deploying, monitoring and managing your AI workloads.

Watch on Red Hat TV

6. Solving your AI adoption challenges

Red Hat AI helps accelerate enterprise AI adoption by enabling small, purpose-built AI models, offering efficient and accessible fine-tuning techniques and providing the flexibility to develop and deploy anywhere. In this on-demand webinar, Alpa Jain will walk you through Red Hat Enterprise Linux AI (RHEL AI) and OpenShift AI, covering what they are, how they work and how they can help you on your AI journey. Jain also explores some real-world AI use cases and the fundamentals of the AI/ML lifecycle.

Here's what you can expect:

• A deep dive into Red Hat’s AI portfolio—What’s available and how it fits into the AI ecosystem
• RHEL AI essentials—Understanding its key components and capabilities
• OpenShift AI in action—How it empowers scalable and efficient AI workloads
• Mastering the AI/ML lifecycle—From model creation through deployment

Watch on Red Hat TV

7. Operationalizing AI with containerized environments, CI pipelines, and model servers

AI is everywhere these days, rapidly changing the future of technology. But behind the scenes, these powerful tools come with challenges—especially when it comes to developing and deploying AI models, integrating them into real-world systems and managing the infrastructure needed to train and run them. Even veteran developers and data scientists can hit roadblocks when moving from experimentation to production.

That’s where MLOps comes in. In this presentation, Jaime Ramírez Castillo breaks down how applying DevOps principles to AI can streamline machine learning workflows, making them more efficient, scalable and reliable. He demonstrates how OpenShift AI provides a hybrid cloud-enabled platform you can use to automate, optimize, manage and deploy your AI models and applications.

Here’s what Castillo covers:

• Model training made simple—Use pre-configured, containerized environments to get started quickly
• Automation at its best—Leverage data science pipelines to streamline the training process
• Streamlined deployment—Move from model development to production with confidence

If you want to take your AI/ML projects to the next level—faster, smarter and with less hassle—this session is for you!

Watch on Red Hat TV

8. Free webinar: Fast-track AI-enabled app delivery with Red Hat OpenShift AI

It's increasingly clear that AI is providing a competitive advantage to organizations that can effectively harness its power, but building a reliable, scalable AI platform isn’t easy. Many struggle with training and deploying models, managing hybrid cloud infrastructure and keeping up with gen AI’s rapid evolution.

OpenShift AI can help simplify this journey. Built on Red Hat OpenShift, it provides a stable, hybrid AI platform that helps data scientists, ML engineers and developers collaborate more efficiently and deploy AI solutions faster.

In this on-demand Red Hat Skill Builders session, Erminio Cassella covers:

• Hybrid cloud AI with OpenShift Containers-as-a-Service (CaaS)
• Predictive AI vs. gen AI
• ML pipelines, model training and serving
• A live MLOps demo with OpenShift AI
• Red Hat Training and Certification to fast-track AI adoption

Watch now

9. Red Hat Developers: Get started with Red Hat OpenShift AI learning paths

If you're just getting started with OpenShift AI, we have a number of learning paths available through Red Hat Developers. These include:

• Introduction to OpenShift AI
• Automation ML pipeline with OpenShift AI
• Demystify RAG with OpenShift AI and Elasticsearch
• Data Engineering: Extract live Data Collection from images and logs

You'll also find a library of articles about Red Hat AI, a no-cost developer sandbox where you can try OpenShift AI and links to our AI training and certification offerings.

Start learning

10. Red Hat training: Developing and deploying AI/ML applications on Red Hat OpenShift AI (with exam)

Master the art of developing and deploying AI applications with OpenShift AI in this hands-on, skill-building course.

Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) equips you with the essential knowledge to harness OpenShift AI for training, developing and deploying AI models. Through interactive labs and real-world scenarios, you'll gain practical experience managing AI workloads efficiently.

This course includes the Red Hat Certified Specialist in OpenShift AI Exam (EX267).

What you'll learn:

• Install and configure OpenShift AI
• Build and manage data science projects
• Work with Jupyter Notebooks for AI/ML development
• Manage users, resources and custom notebook images
• Train and deploy models with OpenShift AI
• Automate workflows with data science pipelines

View the full course description

11. Red Hat consulting: AI platform foundation

Building AI models is a priority for many organizations, but deploying them efficiently can be a challenge. Red Hat Consulting's AI Accelerator option can help you jumpstart your AI journey. In it you'll be paired with Red Hat experts who will help you identify the right tools, integrations and customizations needed to build a scalable, high-performance AI platform.

What you’ll get:

• Tailored AI solutions—Design and deploy an AI platform that fits your needs
• Streamlined integration—Connect data sources, configure hardware accelerators and optimize infrastructure
• Hands-on expertise—Work side-by-side with Red Hat consultants to implement AI workflows
• Custom AI pipelines—Streamline model training and deployment with automation

Red Hat Consulting provides the expertise and support to help turn your AI ambitions into reality—fast, efficient and built to scale.

Contact Red Hat Consulting today

Wrap up

And there you have it—11 resources to help you turn your AI projects from science experiments to fast, efficient and scalable enterprise AI applications.