000			01621nam a22002657i 4500
005			20250722113258.0
008			250722s |||ao||| |||| 00| 0 eng d
020			_a9789389898507
040			_aEG-GaU‬‬ _cEG-GaU‬‬ _dEG-GaU‬‬ _erda
082			_223 _a006.3 _bc.a.c.
100	1		_aChoudhury, Aniruddha, _eauthor. _960426
245	1	0	_aContinuous machine learning with Kubeflow : _bperforming reliable MLOps with capabilities of TFX, Sagemaker and Kubernetes / _cAniruddha Choudhury.
250			_a 1st edition.
264		1	_aNew Delhi : _bBPB Publications, _c2021.
300			_a330 pages : _billustrations ; _c20 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
504			_aIncludes bibliographical references.
505	0		_a1. 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.
520			_aPractical 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}
650			_aMachine learning _xImplementation. _960427
942			_2ddc _cBK
999			_c11064 _d11064