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Module 7: AI/LLM/ML Ops for Beginners

L05: Phases in ML

L05: Phases in ML

7:19

What are the Phases in Machine Learning?

The phases in Machine Learning describe the step-by-step process followed to build an ML solution, from understanding the problem to deploying and improving the model in production.

In simple terms:

ML phases show how raw data becomes an intelligent model that can make predictions.

Main Phases of ML

1. Problem Understanding

Define what needs to be solved and what output is expected.

Example: Predict house prices, detect spam, classify images.

2. Data Collection

Gather relevant data from databases, APIs, sensors, or cloud storage.

3. Data Preparation

Clean and process the data:

  • Handle missing values

  • Remove noise

  • Encode categorical features

  • Normalize numerical values

  • Split into train and test sets

4. Feature Engineering

Select and transform important features to improve model accuracy.

5. Model Selection

Choose suitable algorithms such as:

  • Linear Regression

  • Decision Trees

  • Random Forest

  • Neural Networks

6. Model Training

Train the model using prepared data so it can learn patterns.

7. Model Evaluation

Measure performance using metrics like:

  • Accuracy

  • Precision

  • Recall

  • F1-score

8. Model Deployment

Deploy the trained model into real applications (web apps, APIs, cloud).

9. Monitoring & Improvement

Track model performance, detect drift, and retrain when required.

Why These Phases Are Important

  1. Ensures structured and repeatable development

  2. Improves model accuracy and reliability

  3. Reduces production failures

  4. Supports continuous learning and improvement

  5. Forms the base of MLOps automation

Real-World Example

Spam Email Detection:

  • Collect emails

  • Clean and label data

  • Train classification model

  • Evaluate accuracy

  • Deploy in email system

  • Monitor false positives and retrain

Quiz

Q1. Which phase focuses on cleaning data and handling missing values?

A. Model Training
B. Data Preparation
C. Model Deployment
D. Feature Selection

Correct Answer: B
Explanation: Data preparation includes cleaning, normalization, and transforming raw data into a usable format for training models.

Q2. Which phase ensures the model continues to perform well after deployment?

A. Data Collection
B. Feature Engineering
C. Monitoring & Improvement
D. Model Selection

Correct Answer: C
Explanation: Monitoring and improvement track performance, detect data drift, and trigger retraining to maintain model accuracy over time.