📚 Course Overview

In this cohort-based course, you will delve into the practical aspects of Machine Learning Operations (ML Ops) tailored specifically for data scientists. You'll learn the crucial steps involved in transitioning machine learning models from the experimental phase to robust deployment in real-world production environments.

🎯 What to Expect

By the end of the program, you will gain invaluable insights into:

Understanding ML Ops Fundamentals:

Learn about ML Ops' role in integrating ML models into production and overcome challenges in deploying and managing models effectively.

Structuring Your ML Ops Pipeline:

Explore ML Ops workflows for data preprocessing, model training, deployment, and monitoring, focusing on reproducibility, scalability, and automation with real-world case studies.

Practical Implementation with Project Ideation:

Select a dataset aligned with your interests, create an ML Ops pipeline tailored to your project idea, and apply course concepts for practical application.

Dockerizing ML Training/Inference and Productionizing Notebooks:

Gain insight into Docker's role in streamlining ML project development, with hands-on experience in containerizing ML applications for reproducibility and portability.

Model Training and Inference Preparation:

Discover strategies for ML model deployment preparation, covering dependency packaging, inference optimization, and considerations for local versus cloud deployment.

Git and CI/CD with GitHub Actions, Model Deployment, and SageMaker Integration:

Learn Git for collaborative development, set up CI/CD pipelines with GitHub Actions, and explore AWS SageMaker for scalable ML model deployment, including batch processing and real-time inference.

Exploration of MLOps Case Studies and Capstone Project:

Analyze diverse real-world MLOps implementations for insights, then present your capstone project for feedback, with encouragement to share on GitHub and social media.

🏗️ Course Structure

Participate in 3 deep-dive sessions and 2 one-hour office hours for clarification. Develop a capstone project integrating lesson learnings, which are shareable for skill showcase. Access 20+ curated resources and explore 10+ real-world ML deployment case studies.

🎓 By the end of the course, you will emerge equipped with the knowledge, skills, and confidence to seamlessly transition machine learning models from the lab to production, thus making a tangible impact in your organization's data-driven initiatives.