Join the course Operationalizing ML Models:
MLOps for Scalable AI and dive into turning promising machine learning prototypes into robust, scalable, and maintainable systems. Enhance your understanding through hands-on demos, valuable tools, and case studies from leading companies like Netflix, Uber, and Google.
This program is tailored for data scientists, machine learning engineers, AI practitioners, and IT professionals keen on operationalizing their ML workflows and optimizing their infrastructure management.
Prerequisites include a basic understanding of machine learning concepts, familiarity with Python, and experience with Docker and containerization technologies. By the course's end, you will be adept at operationalizing ML models by designing scalable MLOps workflows, automating deployment through CI/CD pipelines, monitoring performance, detecting data drift, and optimizing AI infrastructure using tools such as Docker, MLflow, and Kubernetes.
These skills will empower you to support real-world AI applications effectively.
Provider:
Coursera
Categories:
Artificial Intelligence Courses, Machine Learning Courses, MLOps Courses, Docker Courses, Kubernetes Courses, CI/CD Courses, Model Deployment Courses, Data Drift Courses, Containerization Courses
