MLOps Engineering Certification

Module 1: Introduction to MLOps

• Understanding MLOps
• ML Lifecycle
• Challenges in Production ML
• MLOps Architecture
• Business Benefits

Module 2: MLOps Fundamentals

• Data Pipelines
• Model Pipelines
• Workflow Automation
• Reproducibility
• Version Control Concepts

Module 3: Python for MLOps

• Python Best Practices
• Virtual Environments
• Dependency Management
• Packaging Projects
• Logging and Configuration

Module 4: Git for Machine Learning Projects

• Source Code Management
• Branching Strategies
• Collaboration Workflows
• Code Reviews
• Repository Organization

Module 5: Data Versioning

• Dataset Management
• Data Lineage
• Version Tracking
• Dataset Validation
• Data Quality Monitoring

Module 6: Feature Engineering Pipelines

• Feature Stores
• Feature Transformation
• Pipeline Automation
• Feature Validation
• Reusable Features

Module 7: Experiment Tracking

• Experiment Management
• Hyperparameter Tracking
• Model Comparison
• Artifact Management
• Reproducibility

Module 8: Model Training Pipelines

• Automated Training
• Pipeline Orchestration
• Distributed Training
• Resource Management
• Workflow Scheduling

Module 9: Model Evaluation

• Performance Metrics
• Validation Techniques
• Bias Detection
• Explainability
• Model Benchmarking

Module 10: Model Registry

• Model Lifecycle Management
• Versioning Models
• Approval Workflows
• Model Metadata
• Release Management

Module 11: CI/CD for Machine Learning

• Continuous Integration
• Continuous Delivery
• Automated Testing
• Deployment Pipelines
• Release Automation

Module 12: Containerization

• Docker Fundamentals
• Building Images
• Container Best Practices
• Multi-Stage Builds
• Container Security

Module 13: Kubernetes for MLOps

• Kubernetes Architecture
• Pods and Deployments
• Services
• Scaling Applications
• Resource Management

Module 14: Cloud-Based MLOps

• Cloud Infrastructure
• Managed ML Services
• Storage Solutions
• Compute Resources
• Cloud Deployment Strategies

Module 15: Model Serving

• Real-Time Inference
• Batch Inference
• API Deployment
• Load Balancing
• Performance Optimization

Module 16: Workflow Orchestration

• Pipeline Scheduling
• Task Dependencies
• Automation Workflows
• Error Handling
• Pipeline Monitoring

Module 17: Monitoring Production Models

• Performance Monitoring
• Drift Detection
• Data Quality Monitoring
• Logging Predictions
• Alerting Systems

Module 18: Security in MLOps

• Identity and Access Management
• Secrets Management
• Secure APIs
• Data Protection
• Infrastructure Security

Module 19: Responsible AI Operations

• Governance Frameworks
• Compliance Best Practices
• Fairness Monitoring
• Explainability
• Risk Management

Module 20: Infrastructure as Code

• Infrastructure Automation
• Configuration Management
• Provisioning Resources
• Environment Consistency
• Deployment Automation

Module 21: Scalable ML Systems

• High Availability
• Distributed Systems
• Resource Optimization
• Cost Management
• Scaling Strategies

Module 22: End-to-End MLOps Pipeline

• Project Planning
• Pipeline Development
• Model Deployment
• Monitoring Implementation
• Continuous Improvement

Module 23: Enterprise MLOps Best Practices

• Organizational Workflows
• Team Collaboration
• Documentation Standards
• Operational Excellence
• Production Readiness

Module 24: Industry Project

• Problem Definition
• Data Pipeline Design
• Automated Training Pipeline
• Production Deployment
• Performance Review