Deploying containerized AI-enabled applications
AI has become an integral part of modern applications, enabling personalized experiences, predictive analytics, automation, and more. Deploying AI-enabled models requires addressing several key issues and a strategy for deployment on a purpose-built platform. Assessing your data’s quality, volume, and relevance and choosing the right model to balance accuracy with performance is critical. But deployment isn't just about the model—it's also about the infrastructure. Organizations must consider the compute resources, storage, and networking requirements as well as legal and ethical standards, to ensure scalability, security, flexibility and ease of use. Red Hat helps to automate the entire AI lifecycle from model development to deployment and monitoring, leading to more reliable AI applications and quicker iteration cycles. This enables organizations to build, deliver, and manage their own AI-enabled applications and services across any cloud, on-premise, or edge environment Join this episode to learn about: - Key considerations before deployment - Choosing a platform for AI deployment - Deployment strategies and best practices
Speakers
Diego Torres Fuerte | Managing Architect, AI Practice Pre-sales, Red Hat
Diego Torres is a software architect with more than 10 years of experience leading customers to implement intelligent applications through the use of process automation, decision management, and artificial intelligence. As the managing architect at AI Practice pre-sales, he leads a talented team of consulting architects that provide AI Consulting services and drive adoption of the Red Hat Openshift AI product. Diego’s background on software development, and technical enablement provides insightful advice to a growing interest in emerging technologies such as predictive and generative AI.