Comprehensive Machine Learning Operations (MLOps) Bootcamp with 10 Real-World Projects

This program is designed to provide a practical and comprehensive journey into the field of Machine Learning Operations (MLOps) through hands-on tutorials and real-world projects. It starts with a solid foundation, covering development environments, Python essentials, and a complete guide to Flask for building and deploying applications. Learners are introduced to version control with Git and GitHub, followed by in-depth lessons on MLflow for experiment tracking and seamless integration into ML and deep learning projects. The course then expands to advanced tools like DVC for data version control, DagsHub for collaboration, and creating end-to-end ML pipelines incorporating Git, DVC, MLflow, and DagsHub. Cloud-based workflows are explored with MLflow on AWS, along with Docker fundamentals through advanced deployment methods. Learners are also exposed to Airflow for building ETL pipelines, including integration with Postgres, APIs, and cloud services like Astro Cloud and AWS. Additionally, the program covers automation with GitHub Actions, complete workflows with DockerHub, and various deployment strategies on AWS and Azure. Learners will work on diverse projects, including network security systems, NLP pipelines with Hugging Face Transformers, and AWS SageMaker for training, deployment, and endpoint creation. The journey concludes with monitoring solutions using Grafana and an introduction to generative AI with AWS LLMOps. By the end of the course, participants will have built over ten industry-grade projects, fully preparing them to face real-world MLOps challenges.