Project Title: MLOps Pipeline

Objective:

To build an automated, end-to-end machine learning operations (MLOps) pipeline that streamlines the development, deployment, monitoring, and maintenance of ML models in production.

Key Components:

Data Ingestion & Preprocessing:

Automated data collection and preprocessing steps.

Version control for datasets using tools like DVC or MLflow.

Model Training & Validation:

Parameterized training scripts to support experimentation.

Use of frameworks like scikit-learn, TensorFlow, or PyTorch.

Model tracking with MLflow, Weights & Biases, or ClearML.

Model Packaging:

Serialize trained models with joblib, pickle, or framework-specific formats.

Include preprocessing steps in reusable pipelines.

Use of Docker for environment consistency.

CI/CD Pipeline:

Automated testing, building, and deployment using GitHub Actions, GitLab CI, or Jenkins.

Integration with container registries and deployment targets.

Model Deployment:

Serve models via REST API using FastAPI or Flask.

Deployment options include Docker, Kubernetes, or cloud services like AWS SageMaker, GCP Vertex AI, or Azure ML.

Monitoring & Logging:

Track model performance, data drift, and prediction accuracy in production.

Use tools like Prometheus, Grafana, Evidently AI, or custom dashboards.

Feedback Loop & Retraining:

Collect real-world data and feedback to retrain and improve models.

Automate retraining triggers based on performance metrics.

Outcome:

A robust, scalable MLOps pipeline that automates the ML lifecycle, ensuring reproducibility, faster deployments, and continuous improvement in a production environment.