Build End-to-End Machine Learning Pipeline

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90 minutes

5 steps

·Advanced

After 90 min: A production ML system that automatically trains, evaluates, and deploys models

What you need

LaptopPythonMLflowDataCloud infrastructure

The 90-Minute Plan

Define Problem0–15 min

Clearly define business problem, success metrics, and model requirements.

Prepare Data15–35 min

Clean, normalize, and split data. Create feature engineering pipeline.

Train & Evaluate35–55 min

Experiment with models. Track experiments using MLflow. Compare results.

Automate Pipeline55–75 min

Create automated workflow using Airflow or similar. Orchestrate from data to predictions.

Ship & next steps75–90 min

Deploy model. Set up monitoring. Next: implement A/B testing.

Pro Tip

Focus on data quality. Document assumptions. Monitor model drift in production.

90 min·5 steps

TechBeginner

After 90 min: Insights extracted from a dataset with visualizations and statistical analysis

9,234

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90 min·5 steps

TechIntermediate

After 90 min: A working predictive model that forecasts outcomes based on historical data

8,234

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90 min·5 steps

TechAdvanced

After 90 min: A reusable library of UI components for your projects and team

9,456

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90 min·5 steps

TechAdvanced

After 90 min: A scalable microservices architecture for a distributed application

7,234

see the plan