ML Lifecycle
Building a machine learning model is not just about writing code. It follows a structured lifecycle, from understanding the problem to deploying the model. This chapter walks you through each stage.
The ML lifecycle is a step‑by‑step process that ensures your model solves the right problem effectively.
The 7 Stages
- Problem Definition: What business problem are you solving? Define success metrics.
- Data Collection: Gather relevant data (databases, APIs, files).
- Data Preparation: Clean, transform, and split data (covered in next module).
- Exploratory Data Analysis (EDA): Visualize and understand patterns.
- Model Building: Choose an algorithm, train on training data.
- Model Evaluation: Test on unseen data, tune hyperparameters.
- Deployment & Monitoring: Put model into production, track performance.
Example: House Price Prediction
- Problem: Predict selling price given features (sq ft, bedrooms, location).
- Data: Past house sales.
- Prepare: Handle missing values, scale numbers.
- EDA: Plot price vs. sq ft – see positive trend.
- Model: Train linear regression.
- Evaluate: Check error on test set.
- Deploy: Create an API that returns price for new house.
Why Follow a Lifecycle?
Without a structured process, you risk building a model that works on paper but fails in the real world. The lifecycle keeps you focused on the goal, ensures reproducibility, and catches issues early.
Two Minute Drill
- ML lifecycle: problem definition → data collection → preparation → EDA → model building → evaluation → deployment.
- Each stage is critical for a successful project.
- Deployment makes the model useful to end users.
- Always start with a clear problem, not with an algorithm.
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