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    Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 94

    Deploy ML Model using Flask

    What is Deploy ML Model using Flask?

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

    Introduction

    What is Deploy ML Model using Flask? Serve predictions via REST endpoints with Flask. Machine learning systems learn patterns from data instead of hard-coded rules.

    Understanding the topic

    How Deploy ML Model using Flask works:

    • Serve predictions via REST endpoints with Flask.
    • Prepare or explore data as needed.
    • Train or apply the model/technique.
    • Evaluate results and iterate.
    TermDescription
    Deploy ML Model using FlaskServe predictions via REST endpoints with Flask
    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 Deploy ML Model using Flask.
    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

    1Deploy ML Model using Flask workflow
    1 / 4

    Understand

    Learn when and why to use Deploy ML Model using Flask.

    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

    Deploy ML Model using Flask is a core machine learning topic. Serve predictions via REST endpoints with Flask

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