Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 96
MLOps
What is MLOps?
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Focus
7 guided sections
Practice signal
Examples included
Career prep
Foundation builder
Introduction
What is MLOps? Operational practices for deploying, monitoring, and maintaining ML in production. Machine learning systems learn patterns from data instead of hard-coded rules.
Understanding the topic
How MLOps works:
- Operational practices for deploying, monitoring, and maintaining ML in production.
- Prepare or explore data as needed.
- Train or apply the model/technique.
- Evaluate results and iterate.
| Term | Description |
|---|---|
| MLOps | Operational practices for deploying, monitoring, and maintaining ML in production |
| Training data | Examples used to learn patterns. |
| Features | Input variables (columns) fed to the model. |
| Target / label | What you predict (supervised learning). |
Step-by-step explanation
- Understand — Learn when and why to use MLOps.
- Prepare data — Load, clean, and split datasets.
- Apply — Fit model or run algorithm in Python/sklearn.
- Evaluate — Measure accuracy, loss, or cluster quality.
Execution workflow
1MLOps workflow
1 / 4Understand
Learn when and why to use MLOps.
Best practices
- Split data into train/validation/test before tuning.
- Scale numeric features when algorithms are distance-based.
- Always evaluate on held-out data — not training accuracy alone.
Common mistakes
- Training on test data (data leakage).
- Ignoring class imbalance in classification metrics.
- Using accuracy alone on imbalanced datasets.
Summary
MLOps is a core machine learning topic. Operational practices for deploying, monitoring, and maintaining ML in production
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