Red Hat AI to fuel innovation

Generative AI (GenAI) has changed the game in terms of new products and service offerings, improved customer experiences, and accelerating efficiency and productivity, but the technology is changing rapidly. Enterprise organizations of every size are under pressure to identify, choose, build, and deliver these AI solutions. They need ready-to-use solutions that provide a quick ramp up and allow scaling at their own pace without making large investments or having a strong AI knowledge base. The key challenges that they face include: - Option fatigue. They are met with too many options and approaches for getting started with their AI projects. - Budget, hardware, and data requirements. There is no one-answer-fits-all solution. Both predictive and generative AI models require large data sets, skilled personnel, and specialty hardware and deciding where to train or deploy models can affect budget, hardware accessibility, and data concerns. - Scale across the model and application lifecycle.. Once models are ready to be incorporated into existing and new applications, then a new set of challenges arises such as: scalability, resource management (storage and hardware), lifecycle management, and monitoring. As more and more applications are modernized with AI, enterprise organizations will need to consider the impact on IT operations and the effort it takes to manage and automate the lifecycle of both models and applications.