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Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems (From Scratch)
KWD 22
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A must-have if you want to learn how to build and deploy ML models from scratch.
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Product Details
| Publisher | Manning Publications |
| Publication date | March 10, 2026 |
| Language | English |
| Print length | 504 pages |
| ISBN-10 | 1633437337 |
| ISBN-13 | 978-1633437333 |
| Item Weight | 13.7 ounces (388.4 grams) |
| Dimensions | 7.38 x 1.26 x 9.25 inches (18.7 x 3.2 x 23.5 cm) |
Product Description
Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems (From Scratch)
Customer Questions & Answers
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Question:
What kind of projects will I learn in this book?
Answer: You will work on hands-on projects like building data pipelines for image classifiers and recommendation systems. -
Question:
Which tools will I learn to use in machine learning?
Answer: The book covers tools such as Kubeflow, MLflow, BentoML, and more for effective MLOps. -
Question:
Is this book suitable for beginners in machine learning?
Answer: Yes, it provides a structured introduction to building and deploying ML models from scratch.
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Features & Benefits
- Learn to design and operate a production-ready machine learning platform.
- Hands-on projects such as image classifiers and recommendation systems.
- Master MLOps fundamentals like orchestration and deployment.
- Use real open-source tools like Kubeflow and MLflow.
- Streamline workflows and enhance collaboration between teams.
- Achieve automation and improve model deployment efficiency.
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