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Practical full‑stack machine learning : A guide to build reliable, reusable, and production‑ready full‑stack ML solutions / Alok Kumar.

By: Material type: TextTextNew Delhi : BPB Publications, 2021Edition: 1st editionDescription: 422 pages : illustrations ; 20 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9789391030421
Subject(s): DDC classification:
  • 23 006.3 k.a.p.
Contents:
1. Organizing Your Data Science Project ‡ 2. Preparing Your Data Structure ‡ 3. Building Your ML Architecture ‡ 4. Bye‑Bye Scheduler, Welcome Airflow ‡ 5. Organizing Your Data Science Project Structure ‡ 6. Feature Store for ML ‡ 7. Serving ML as API.
Summary: Practical guide introducing production‑ready full‑stack ML pipelines using Python. Covers feature engineering, dataops, Airflow, Dask, MLflow, SageMaker, FastAPI, and scalable API deployment.:contentReference[oaicite:1]{index=1}
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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Books Books Media and mass communication Library B4 006.3 k.a.p C.1 Available MA0002511
Total holds: 0

Includes bibliographical references.

1. Organizing Your Data Science Project ‡ 2. Preparing Your Data Structure ‡ 3. Building Your ML Architecture ‡ 4. Bye‑Bye Scheduler, Welcome Airflow ‡ 5. Organizing Your Data Science Project Structure ‡ 6. Feature Store for ML ‡ 7. Serving ML as API.

Practical guide introducing production‑ready full‑stack ML pipelines using Python. Covers feature engineering, dataops, Airflow, Dask, MLflow, SageMaker, FastAPI, and scalable API deployment.:contentReference[oaicite:1]{index=1}

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