MLOps

Overview of MLOps and its importance

Common challenges in ML development and deployment

Key principles of MLOps

Introduction to Git and GitHub

Best practices for version controlling ML projects

Branching and merging strategies

Managing data and model artifacts with Git LFS

Introduction to Docker and containerization

Building Docker images for ML applications

Running containers locally and on the cloud

Best practices for containerizing ML applications

Overview of CI/CD pipelines for ML

Setting up a CI/CD pipeline with Jenkins

Automating Testing and Validation of ML Models

Deploying ML models with Kubernetes

Importance of monitoring and logging in MLOps

Setting up monitoring and logging infrastructure

Defining metrics and alerts for ML models

Visualizing and analyzing model performance

Introduction to distributed computing for ML

Setting up a distributed ML environment with Apache Spark

Scaling ML workloads with Kubernetes and Docker Swarm

Best practices for scaling ML workloads

Overview of ethical considerations in MLOps

Bias and fairness in ML models

Privacy and security considerations in MLOps

Implementing ethical guidelines in MLOps