Machine Learning Operations

Aims

Machine Learning Operations will bridge the gap between theoretical knowledge and practical application in the field of Machine Learning (ML) and Artificial Intelligence (AI). The aim of this microcredential is to equip learners with a deeper understanding of MLOps through hands-on practice and project work, ensuring that participants are well-prepared to develop, deploy, and maint ML and AI applications to address complex real-world problems.

Content

Foundations of Machine Learning and MLOps

• Introduction to Machine Learning (ML) concepts.
• Data preparation and pre-processing techniques.
• Model development and evaluation.evaluation.
• Introduction to MLOps and its significance.
• Setting up the MLOps environment: Tools and platforms.

MLOps Tools and Version Control (I)

• Deep dive into ML/AI tools (TensorFlow, PyTorch).
• Version control systems (Git) for ML projects.
• Hands-on project: Implementing ML/AI and manage using VCS.

MLOps Tools and Version Control (II)

• Containerization with Docker and Kubernetes in ML.
• Continuous Integration and Continuous Deployment (CI/CD) in ML.
• Hands-on project: Implementing CI/CD in an ML project.

Advanced MLOps Techniques (I)

• Advanced model training techniques.
• Hyperparameter tuning and optimization.
• Project work: Building and training an ML model.

Advanced MLOps Techniques (II)

• Model serving and deployment strategies.
• Monitoring and logging for ML models in production.
• Project work: Deploying and monitoring an ML model.

Real-World Applications and Capstone Project

• Case studies: Successful MLOps implementations.
• Industry speakers and Q&A sessions.
• Capstone project ideation and planning.
• Capstone project development.
• Capstone project presentations and feedback.