The podcast addresses the growing importance of AI cloud engineers in the current tech landscape. It highlights that while much attention is given to AI platforms, the cloud infrastructure underpinning these systems is critical, focusing on bridging cloud infrastructure with AI systems to create competitive advantages for businesses. The discussion covers three phases to becoming an AI Cloud Engineer, emphasizing cloud fundamentals such as compute options (EC2, containers, Lambda), storage solutions (data lakes, S3), networking demands (data ingestion, distributed training, inference serving), and identity/access management using attribute-based access control. It also advocates for Infrastructure as Code (IAC) and Python to manage AI workloads, and introduces a retail inventory management system project using AWS Bedrock to predict demand and recommend products, secured with role-based access controls, network protections, and end-to-end encryption. Finally, it introduces MLOps to continuously improve AI models.
Sign in to continue reading, translating and more.
Continue
