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Lessons > Experiment Tracking & Reproducibility

Experiment Tracking & Reproducibility

Course Content
Day 1
0/3
Introduction to MLOps
00:20:00
ML Project Lifecycle
00:20:00
Hands-on: Set Up a Git-Driven ML Project Repository
00:20:00
Day 2
0/3
Experiment Tracking & Reproducibility
00:20:00
Versioning Code, Data & Models
00:20:00
Hands-on: Track and Compare Experiments with MLflow + DVC
00:20:00
Day 3
0/3
Model Training Pipelines
00:20:00
Pipeline Orchestration
00:20:00
Hands-on: Build a Training Pipeline with Prefect or Airflow
00:20:00
Day 4
0/3
Model Deployment Strategies
00:20:00
Serving Models with REST APIs
00:20:00
Hands-on: Deploy a Model as a REST API using FastAPI
00:20:00
Day 5
0/3
CI/CD for Machine Learning
00:20:00
ML Deployment with Docker & Kubernetes
00:20:00
Hands-on: Build a CI/CD Pipeline for ML with GitHub Actions + Docker
00:20:00
Day 6
0/3
Model Monitoring & Drift Detection
00:20:00
Tools for Monitoring
00:20:00
Hands-on: Set Up Drift Detection & Monitoring Dashboards
00:20:00
Day 7
0/3
Model Registry & Lifecycle Management
00:20:00
Best Practices in Production Model Governance
00:20:00
Hands-on: Register and Serve a Model with MLflow Registry
00:20:00
Day 8
0/3
Cloud-Native MLOps Platforms
00:20:00
Deploying Models to HuggingFace or Streamlit Cloud
00:20:00
Hands-on: Deploy an ML App on HuggingFace Spaces or Streamlit Cloud
00:20:00
Day 9
0/3
Security, Compliance & Ethics in MLOps
00:20:00
Audit Logs and Access Controls
00:20:00
Hands-on: Add Role-Based Access to MLflow Deployment
00:20:00
Day 10
0/2
Capstone Day: Design & Deploy a Full MLOps Project
00:30:00
Final Presentation + Infra Diagram
00:30:00
MLOps – Machine Learning Operations for Developers & Data Teams

Use tools like MLflow and Weights & Biases to log experiments, track metrics, and manage runs.

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