ISBN: 9781633437333 - machine learning platform engineering: build an internal developer platform for ml and ai systems (21 risultati)

paperback. Condizione: Fine.

Condizione: As New. Unread book in perfect condition.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: New.

Condizione: new.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Paperback. Condizione: Brand New. 325 pages. 9.26x7.38x9.25 inches. In Stock.

paperback. Condizione: New. Special order direct from the distributor.

Condizione: New. 2026. 1st Edition. paperback. . . . . .

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Paperback. Condizione: Brand New. 325 pages. 9.26x7.38x9.25 inches. In Stock.

Condizione: New. 2026. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Condizione: NEW.

Buch. Condizione: Neu. Neuware - Machine learning models look great in not Elektronisches Buch, then collapse in production. Ready to build an ML platform that actually delivers Here's a step-by-step, project-driven guide to building an MLOps-ready platform from scratch. Inside you'll find: Step-by-step ML pipeline assembly: A true 'from-scratch' playbook that assembles an end-to-end MLOps stack. Deploy machine learning models to production: Combine Kubeflow, MLflow, BentoML, Feast, and Evidently without vendor lock-in. Build end-to-end data pipelines: Move seamlessly from raw data to monitored, live predictions. Robust deployment patterns: Serve fast, scalable models that stay responsive under real traffic. Effective monitoring and explainability: Detect drift early and keep stakeholders confident. Build a Machine Learning Platform (From Scratch) by Benjamin Tan Wei Hao, Shanoop Padmanabhan, and Varun Mallya delivers a practical field guide in print and Elektronisches Buch formats. Three veteran engineers lead you through every layer of modern MLOps. The chapters construct two reference systems, an image classifier and a recommendation engine, while teaching orchestration, training, serving, and monitoring techniques. The actionable items for each concept include sample code, architecture diagrams, and checklists. By the end of this book, you will end up with a reusable blueprint that slashes deployment time, reduces firefighting, and thrives with team growth. You will start shipping platforms that thrive. Ideal for Python-savvy data scientists and software engineers eager to master production-quality machine learning. …

Buch. Condizione: Neu. Build a Machine Learning Platform (From Scratch) | Benjamin Hao | Buch | Englisch | 2026 | Manning Publications | EAN 9781633437333 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Buch. Condizione: Neu. Neuware -Machine learning models look great in not Elektronisches Buch, then collapse in production. Ready to build an ML platform that actually delivers Here's a step-by-step, project-driven guide to building an MLOps-ready platform from scratch. Inside you'll find: Step-by-step ML pipeline assembly: A true 'from-scratch' playbook that assembles an end-to-end MLOps stack. Deploy machine learning models to production: Combine Kubeflow, MLflow, BentoML, Feast, and Evidently without vendor lock-in. Build end-to-end data pipelines: Move seamlessly from raw data to monitored, live predictions. Robust deployment patterns: Serve fast, scalable models that stay responsive under real traffic. Effective monitoring and explainability: Detect drift early and keep stakeholders confident. Build a Machine Learning Platform (From Scratch) by Benjamin Tan Wei Hao, Shanoop Padmanabhan, and Varun Mallya delivers a practical field guide in print and Elektronisches Buch formats. Three veteran engineers lead you through every layer of modern MLOps. The chapters construct two reference systems, an image classifier and a recommendation engine, while teaching orchestration, training, serving, and monitoring techniques. The actionable items for each concept include sample code, architecture diagrams, and checklists. By the end of this book, you will end up with a reusable blueprint that slashes deployment time, reduces firefighting, and thrives with team growth. You will start shipping platforms that thrive. Ideal for Python-savvy data scientists and software engineers eager to master production-quality machine learning. 504 pp. Englisch. …