AI Agents and MLOps for Production-Ready AI

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain in-depth knowledge and hands-on experience with AI agents and MLOps, crucial components for developing and deploying production-ready AI solutions. You will begin by exploring various AI agents, including AutoGen, IBM Bee, LangGraph, CrewAI, and AutoGPT. The course provides practical insights on how these frameworks can automate AI workflows and create autonomous AI agents. You will have the opportunity to implement these agents, developing AI-driven systems that can carry out tasks like decision-making, automation, and optimization. The second part of the course delves into MLOps, focusing on the operationalization of machine learning models. You’ll explore MLOps concepts such as versioning, automation, and monitoring, and how they fit into the broader context of machine learning deployment. Through hands-on exercises, you will learn to set up MLOps environments using tools like Git, Docker, and Kubernetes, and develop end-to-end machine learning pipelines. The course emphasizes the critical differences between experimentation and production in machine learning, teaching you how to build robust systems that can seamlessly move from development to deployment. The course also covers the necessary infrastructure for MLOps, including cloud platforms like AWS, GCP, and Azure, and how to containerize models using Docker. You will gain practical skills in deploying and managing machine learning models at scale using Kubernetes, ensuring your models are production-ready and scalable. This comprehensive journey will provide you with the tools to manage ML workflows, optimize deployment processes, and integrate AI agents into production environments. This course is designed for AI practitioners, data scientists, and en