Phases of a Machine Learning Project

1. Define Business Goals

Every ML project starts with defining the business objective.
Stakeholders must agree on:
- The value the project will provide
- The budget
- The success criteria
KPI (Key Performance Indicators) are critical to measure whether
the ML model actually achieves business goals.

👉 Exam Tip: AWS often asks about the importance of KPIs in framing an ML project. The first step is always business problem definition, not jumping into training a model.

2. Frame the Problem as an ML Problem

Convert the business problem into a machine learning problem.
Ask: Is machine learning the right tool? Sometimes rules-based systems are more appropriate.
Collaboration is key: data scientists, data engineers, ML architects, and subject matter experts (SMEs) must all contribute.

👉 Example:

Business problem: “How can we reduce customer churn?”
ML problem: “Predict whether a customer will leave in the next 30 days.”

3. Data Processing

Data collection and integration: Centralize data into a usable location (e.g., Amazon S3).
Data preprocessing: Clean, normalize, handle missing values.
Data visualization: Understand data patterns and spot anomalies.
Feature engineering: Create or transform variables that help the model learn.

👉 AWS Services:

AWS Glue for ETL (extract, transform, load)
Amazon QuickSight for visualization
Amazon S3 for data storage

4. Exploratory Data Analysis (EDA)

Visualize data distributions and trends using charts.
Correlation Matrix: Measures how strongly variables are related.
- Example: Study hours ↔ Test score correlation of 0.85 shows a strong positive relationship.
Helps you decide which features are most valuable for your model.

👉 Exam Tip: Feature selection and correlation analysis often appear in ML exam scenarios.

5. Model Development

Model training: Fit the model with training data.
Model tuning: Adjust hyperparameters (e.g., learning rate, number of trees).
Model evaluation: Test against validation or test datasets.
This process is iterative:
- Go back and improve features.
- Try different algorithms.
- Tune hyperparameters repeatedly.

👉 AWS Services:

Amazon SageMaker provides: - Model training
Automatic hyperparameter tuning
Built-in evaluation metrics

6. Retraining

As new data arrives, retrain the model to keep it relevant.
Adjust features and hyperparameters based on performance.

7. Deployment

Once the model meets goals, it is deployed for predictions (inference).
Deployment options:
- Real-time (low-latency APIs)
- Batch (large-scale predictions at once)
- Serverless (cost-efficient, scalable)
- On-premises (for compliance or offline needs)

👉 AWS Services:

SageMaker Endpoints: real-time inference
Batch Transform: batch inference
Serverless Inference: scalable, cost-optimized

8. Monitoring

Ensure the model maintains expected performance.
Early detection of problems such as model drift (when new data no longer matches training patterns).
Debugging and understanding behavior in production.

👉 AWS Service:

SageMaker Model Monitor automatically detects drift, anomalies, and performance degradation.

9. Iterations and Continuous Improvement

ML projects are never “one-and-done.”
As new data becomes available:
- Retrain
- Deploy again
- Monitor results
Requirements may change over time.
Example: A clothing recommendation model must be retrained regularly as fashion trends evolve.

👉 Exam Tip: AWS emphasizes continuous retraining and monitoring to keep ML models accurate and relevant.

Workflow Summary

Define business goals & KPIs
Frame as an ML problem
Collect & process data
Perform EDA and feature engineering
Train, tune, and evaluate the model
Retrain when needed
Deploy (real-time, batch, serverless, on-prem)
Monitor performance & drift
Iterate for continuous improvement

✅ Key Takeaways for Exams: - The first step = business goals + KPI definition.

EDA and correlation matrices help identify key features.
SageMaker supports training, tuning, deployment, and monitoring.
Know the differences between real-time vs batch vs serverless inference.
Monitoring and retraining are critical due to model drift.