The 5 pillars of an AI strategy

The rapid democratization of AI has not gone unnoticed. Organizations of all sizes, across the globe are now seeking ways to capitalize on this trend. However, the swift advancements in the AI ecosystem come with their own set of challenges. These include selecting the right model, deploying and re-deploying it into the appropriate environment, using different techniques such as Retrieval-Augmented Generation (RAG) for context awareness, or applying effective fine-tuning techniques. Organizations are now faced with the difficult task of building enterprise-grade, robust, secure, and adaptable AI solutions to achieve their desired outcomes. In the current landscape of enterprise AI, developing generative and predictive AI solutions, and leveraging Large Language Models (LLM) faces significant challenges such as dependency on specific model providers, black-box customization issues, and difficulties keeping up with constantly updated open-source machine learning frameworks. This webinar introduces Red Hat's comprehensive open-source approach to overcoming these obstacles, offering scalable and cost-effective solutions for generative AI application development. In this session you will learn about: - Understanding Challenges: Dive into different issues such as data access, tooling proliferation, model dependency, and scalable MLOps approaches - Red Hat’s Innovative Solutions: Discover how Podman AI Lab, Red Hat Enterprise Linux AI (RHEL AI), Image Mode for RHEL, and OpenShift can simplify your AI development process, reducing costs and enhancing efficiency (DEMO). - Scalable AI Operations: Learn about OpenShift AI’s role in enhancing MLOps capabilities, ensuring your AI projects are scalable and manageable. Join us to explore how Red Hat's innovative artificial intelligence strategy can revolutionize your enterprise initiatives and propel the development of advanced generative AI applications.