MLOps - Machine Learning Lifecycle Management
This project demonstrates a complete MLOps (Machine Learning Operations) pipeline for managing the machine learning lifecycle. It integrates three key tools: Great Expectations for data validation, Prefect for workflow orchestration, and MLflow for experiment tracking and model management. The project includes data loading, preprocessing, model training, validation, deployment, and serving capabilities. It supports both Docker-based deployment and local development, with comprehensive tooling for model versioning, experiment tracking, and automated workflows. The system includes FastAPI for model serving, Jupyter Lab for development, and Docker containerization for consistent environments.
Overview
This project demonstrates a complete MLOps (Machine Learning Operations) pipeline for managing the machine learning lifecycle. It integrates three key tools: Great Expectations for data validation, Prefect for workflow orchestration, and MLflow for experiment tracking and model management. The project includes data loading, preprocessing, model training, validation, deployment, and serving capabilities. It supports both Docker-based deployment and local development, with comprehensive tooling for model versioning, experiment tracking, and automated workflows. The system includes FastAPI for model serving, Jupyter Lab for development, and Docker containerization for consistent environments.
Key Features
Data validation with Great Expectations
Workflow orchestration with Prefect
Experiment tracking and model registry with MLflow
Model serving with FastAPI REST API
Docker containerization for deployment
Multi-service integration (MLflow, Prefect, FastAPI, Jupyter)
Automated data quality checks
Task scheduling and dependency management
Model versioning and artifact storage
Development environment with Jupyter Lab
pages.portfolio.projects.mlops_lifecycle_management.features.10
Technical Highlights
Integrated Great Expectations, Prefect, and MLflow for complete MLOps pipeline
Implemented automated data validation and quality assurance
Created workflow orchestration with Prefect for task management
Set up comprehensive experiment tracking with MLflow
Deployed model serving API with FastAPI
Containerized entire system with Docker for consistent deployment
Challenges and Solutions
Tool Integration
Integrated multiple MLOps tools seamlessly with standardized interfaces and configuration management
Data Validation
Ensured data quality throughout the pipeline using Great Expectations with automated validation checks
Workflow Orchestration
Managed complex ML workflows with dependencies using Prefect for task orchestration
Experiment Tracking
Achieved reproducibility and model versioning using MLflow for comprehensive experiment tracking
Model Deployment
Ensured consistent deployment across environments using Docker containerization with MLflow model serving
Multi-Service Management
Managed multiple services (MLflow, Prefect, FastAPI, Jupyter) using Docker multi-service container
Technologies
MLOps Tools
API
Development
Data
Visualization
Environment
Project Information
- Status
- Completed
- Year
- 2025
- Architecture
- MLOps Pipeline Architecture with Data Validation, Workflow Orchestration, and Model Management
- Category
- Data Science