Unit 1: MLOps Fundamentals
Unit 2: The MLOps Stack
Unit 3: Setting Up Your MLOps Environment
MLOps for Python ML Engineers
Master MLOps principles and tools to build, deploy, and monitor production-ready machine learning models with confidence.
Unit 1: Git for Machine Learning Projects
Unit 2: MLflow Tracking Fundamentals
Unit 3: Advanced MLflow and Git Integration
Unit 1: Data Validation Fundamentals
Unit 2: Automated Data Preprocessing
Unit 3: Building Reusable Pipelines
Unit 1: CI Fundamentals and Setup
Unit 2: Automated Testing for ML Code
Unit 3: Integration and Model Validation
Unit 1: Introduction to Continuous Delivery for ML Models
Unit 2: Deployment Strategies
Unit 3: CI/CD Integration and Rollbacks
Unit 4: Configuration Management and Security
Unit 1: Docker Fundamentals for ML
Unit 2: Dockerizing ML Applications
Unit 3: Advanced Docker Techniques
Unit 1: Kubernetes Fundamentals for MLOps
Unit 2: Deploying ML Models on Kubernetes
Unit 3: Advanced Kubernetes Concepts for MLOps
Unit 1: Terraform Fundamentals
Unit 2: Terraform State Management
Unit 3: Provisioning ML Infrastructure
Unit 1: Introduction to Model Monitoring
Unit 2: Setting Up Monitoring Infrastructure
Unit 3: Implementing Alerting and Automated Retraining
Unit 4: Advanced Monitoring Techniques
Unit 5: Case Studies and Best Practices
Unit 1: Introduction to Cloud MLOps
Unit 2: MLOps on AWS SageMaker
Unit 3: MLOps on Azure ML
Unit 4: MLOps on GCP Vertex AI
Unit 1: Secrets Management and Data Encryption
Unit 2: Access Control and Adversarial Attack Prevention
Unit 3: Security Monitoring and Auditing