Introduction to MLOps and the Machine Learning Lifecycle

Goal: Understand what MLOps is, why it exists, and how it differs from DevOps.

Estimated Time: 60–90 minutes

Learning Objectives

By the end of this lesson, you should be able to:

• Explain MLOps in your own words.
• Understand the end-to-end ML lifecycle.
• Describe why deploying ML models is harder than deploying traditional software.
• Identify the major components of an MLOps platform.

What is MLOps?

MLOps (Machine Learning Operations) is the discipline of applying engineering, DevOps, and data engineering practices to build, deploy, monitor, and maintain machine learning systems in production.

Think of it as:

DevOps + Data Engineering + Machine Learning Engineering + Monitoring + Governance

Traditional DevOps answers:

“How do we reliably deploy software?”

MLOps answers:

“How do we reliably deploy software and continuously improve machine learning models using changing data?”

Why DevOps Alone is not Enough

Input
↓
Application
↓
Output

Machine learning system behave differently

Training Data
      ↓
Feature Engineering
      ↓
Model Training
      ↓
Model
      ↓
Predictions
      ↓
Feedback
      ↓
Retraining

Unlike software:

• Data changes
• User behavior changes
• Models degrade
• Features evolve
• Labels arrive later

This introduces new operational challenges.

Traditional Software vs Machine Learning

Why Models Fail

Example: Model Performance Degradation in an Email Spam Detection System

Consider a machine learning model designed to classify incoming emails as spam or legitimate.

The model was trained using email data collected in Y2025, allowing it to learn the spam patterns, message structures, keywords, sender characteristics, and behavioral signals present at that time.

Training environment

2025 Email Data → Model Training → 98% Accuracy

When the model is deployed to production in 2026, the underlying application and infrastructure continue to operate normally. Requests are processed successfully, the inference API remains available, and predictions are returned without errors.

However, spam behavior has evolved.

Spammers may introduce new techniques such as:

• New message templates and vocabulary
• AI-generated phishing and spam content
• New URL-obfuscation techniques
• Changes in sender and domain behavior
• Previously unseen attack patterns

As a result, the characteristics of production data begin to differ from the data the model originally learned from.

Production environment

2026 Email Data → Changed Spam Patterns → Model Accuracy Drops from 98% to 79%

This illustrates an important difference between traditional software systems and machine learning systems:

A machine learning application can be operationally healthy while its model is producing increasingly inaccurate predictions.

This degradation in model effectiveness over time is commonly associated with model drift. The statistical properties of the data, the relationship between input features and outcomes, or real-world behavior may change after deployment, causing the model’s production performance to deteriorate.

Machine Learning Lifecycle

The machine learning lifecycle is an iterative process that transforms business requirements and data into production models, with continuous monitoring and feedback driving retraining and improvement continuously.

Components of MLOps

A production ML platform typically includes:

Typical Team Responsibilities

MLOps Pipeline Example

Suppose you’re building a house-price prediction system. Here every arrow can be automated.

Raw CSV
      ↓
Validation
      ↓
Cleaning
      ↓
Feature Engineering
      ↓
Training
      ↓
Evaluation
      ↓
Model Registry
      ↓
Docker Image
      ↓
Kubernetes
      ↓
Prediction API
      ↓
Monitoring
      ↓
Retraining

Real-World Example

Consider a recommendation engine for an e-commerce platform.

Daily process:

1. Collect user clicks and purchases.
2. Generate updated features.
3. Train a new recommendation model.
4. Compare it with the current production model.
5. If metrics improve and validation passes, register the new model.
6. Deploy the new model using a CI/CD pipeline.
7. Monitor latency, accuracy, and business metrics.
8. Trigger retraining if performance degrades.

This continuous cycle is the essence of MLOps.

MLOps Stack (Overview)

For the MLOps tutorial, I would present the stack by capability, because enterprises rarely use one platform for everything. They typically combine cloud-native services, commercial platforms, and open-source components

Quiz