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Continuous machine learning with Kubeflow : performing reliable MLOps with capabilities of TFX, Sagemaker and Kubernetes / Aniruddha Choudhury.

By: Material type: TextTextPublisher: New Delhi : BPB Publications, 2021Edition: 1st editionDescription: 330 pages : illustrations ; 20 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9789389898507
Subject(s): DDC classification:
  • 23 006.3 c.a.c.
Contents:
1. Introduction to Kubeflow & Kubernetes cloud architecture ‡ 2. Developing Kubeflow pipeline in GCP ‡ 3. Designing computer vision model in Kubeflow ‡ 4. Building TFX pipeline ‡ 5. ML model explainability & interpretability ‡ 6. Building Weights & Biases pipeline development ‡ 7. Applied ML with AWS SageMaker ‡ 8. Web app development with Streamlit & Heroku.
Summary: Practical guide to deploying and managing continuous ML pipelines using Kubeflow on Kubernetes. Covers TFX, SageMaker, Explainable AI, and cloud deployments with Docker, GCP, AWS, and Heroku. Ideal for MLOps/devops engineers and data scientists. :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 006.03 a.s.f C.1 Available MA0002286
Total holds: 0

Includes bibliographical references.

1. Introduction to Kubeflow & Kubernetes cloud architecture ‡ 2. Developing Kubeflow pipeline in GCP ‡ 3. Designing computer vision model in Kubeflow ‡ 4. Building TFX pipeline ‡ 5. ML model explainability & interpretability ‡ 6. Building Weights & Biases pipeline development ‡ 7. Applied ML with AWS SageMaker ‡ 8. Web app development with Streamlit & Heroku.

Practical guide to deploying and managing continuous ML pipelines using Kubeflow on Kubernetes. Covers TFX, SageMaker, Explainable AI, and cloud deployments with Docker, GCP, AWS, and Heroku. Ideal for MLOps/devops engineers and data scientists. :contentReference[oaicite:1]{index=1}

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