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    Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 90

    Machine Learning Deployment

    What is Machine Learning Deployment?

    Course progress0%
    Focus
    7 guided sections
    Practice signal
    Examples included
    Career prep
    Foundation builder

    Introduction

    What is Machine Learning Deployment? Integrate trained models into apps and services for real predictions. Machine learning systems learn patterns from data instead of hard-coded rules.

    Understanding the topic

    How Machine Learning Deployment works:

    • Integrate trained models into apps and services for real predictions.
    • Prepare or explore data as needed.
    • Train or apply the model/technique.
    • Evaluate results and iterate.
    TermDescription
    Machine Learning DeploymentIntegrate trained models into apps and services for real predictions
    Training dataExamples used to learn patterns.
    FeaturesInput variables (columns) fed to the model.
    Target / labelWhat you predict (supervised learning).

    Step-by-step explanation

    1. Understand — Learn when and why to use Machine Learning Deployment.
    2. Prepare data — Load, clean, and split datasets.
    3. Apply — Fit model or run algorithm in Python/sklearn.
    4. Evaluate — Measure accuracy, loss, or cluster quality.

    Execution workflow

    1Machine Learning Deployment workflow
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    Understand

    Learn when and why to use Machine Learning Deployment.

    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

    Machine Learning Deployment is a core machine learning topic. Integrate trained models into apps and services for real predictions

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