DevOps, DataOps, MLOps

This course is a part of MLOps | Machine Learning Operations, a 4-course Specialization series from Coursera.

Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications. This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML. By the end of the course, you will be able to use web frameworks (e.g., Gradio and Hugging Face) for ML solutions, build a command-line tool using the Click framework, and leverage Rust for GPU-accelerated ML tasks. Week 1: Explore MLOps technologies and pre-trained models to solve problems for customers. Week 2: Apply ML and AI in practice through optimization, heuristics, and simulations. Week 3: Develop operations pipelines, including DevOps, DataOps, and MLOps, with Github. Week 4: Build containers for ML and package solutions in a uniformed manner to enable deployment in Cloud systems that accept containers. Week 5: Switch from Python to Rust to build solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and MLOps.