Before diving into MLOps, let's understand two important things: What is Machine Learning? And what is a Model?
What is Machine Learning?
Machine Learning (ML) is a way to teach computers to learn from examples.
In normal programming, you write rules:
"If email has 'free money', mark it spam."
In Machine Learning, you don't write rules. Instead, you show the computer many examples. It figures out the rules by itself.
Think About It Like This
Imagine teaching a kid to spot cats:
- You don't explain what a cat looks like
- You just show them 100 photos of cats
- After seeing enough cats, they can spot new cats on their own
That's exactly how Machine Learning works. You give examples, the computer learns patterns.
What is a Model?
A model is what the computer creates after learning. It holds all the patterns it found.
The Student Example
Think of a student preparing for a math exam:
Textbook (Data)
You give the student 100 math problems. Each problem has the answer shown.
Studying (Training)
The student goes through all problems. They figure out which formulas to use.
Cheat Sheet (Model)
The student writes down all the formulas they learned. This cheat sheet is the model.
Exam (Prediction)
Now you give new problems without answers. The student uses their cheat sheet to solve them.
The model is like that cheat sheet. It holds what was learned and helps answer new questions.
Types of Machine Learning
There are three main types:
Supervised Learning
You give the computer examples WITH answers. Like a teacher grading homework and showing what's right or wrong.
Example: You label 1000 emails as "spam" or "not spam". The computer learns the difference.
Unsupervised Learning
You give examples WITHOUT answers. The computer finds patterns on its own.
Example: Group customers who buy similar things. No labels needed.
Reinforcement Learning
The computer learns by doing. It tries things, gets rewards for good moves, and learns from mistakes.
Example: A game AI plays thousands of games and gets better over time.
Why This Matters for MLOps
Now you know what ML and models are. But using them in real apps brings new questions:
- Where do we store all the training data?
- How do we train models automatically?
- How do we know if a model works well?
- How do we put models into apps people use?
- How do we update models when they get old?
MLOps is the practice that answers all these questions. It's like DevOps, but for Machine Learning.
Key Takeaways
- Machine Learning = Teaching computers to learn from examples
- Model = The patterns the computer learned (like a cheat sheet)
- Training = The process of learning from data
- Prediction = Using the model to answer new questions
Next: How to Build an ML Model - from data to predictions