Books / AI and ML / MLOps

AI and ML

All Programming languages 1294AI and ML 661Algorithms and theory 471Software Development 391WEB development 334DevOps and Infrastructure 233Cybersecurity 218Databases 144OS and System 143Game development 122Embedded systems 119Other 109IT management 100
All Data Science 209Machine learning 133Neural networks 87LLM 85ChatGPT 31TensorFlow 27Computer Vision 24PyTorch 19MLOps 17Natural Language Processing 12熊猫 9AI Agents 1
EN
Learning AutoML: Automating ML Pipelines with AutoGluon, Leading Frameworks, and Real-World Integration

Learning AutoML: Automating ML Pipelines with AutoGluon, Leading Frameworks, and Real-World Integration

A

Kerem Tomak

AI and ML
EN
Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems

Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems

A

Benjamin Tan Wei Hao, Shanoop Padmanabhan, Varun Mallya

AI and ML
EN
Machine Learning System Design: With End-to-End Examples

Machine Learning System Design: With End-to-End Examples

A

Valerii Babushkin, Arseny Kravchenko

AI and ML
EN
Architecting AI Software Systems: Crafting robust and scalable AI systems for modern software development

Architecting AI Software Systems: Crafting robust and scalable AI systems for modern software development

A

Richard D Avila, Imran Ahmad

AI and ML
EN
Data Engineering for Machine Learning Pipelines: From Python Libraries to ML Pipelines and Cloud Platforms

Data Engineering for Machine Learning Pipelines: From Python Libraries to ML Pipelines and Cloud Platforms

A

Pavan Kumar Narayanan

AI and ML
EN
MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations

MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations

A

Hien Luu, Max Pumperla, Zhe Zhang

AI and ML
EN
LLM Engineer’s Handbook: Master the art of engineering large language models from concept to production

LLM Engineer’s Handbook: Master the art of engineering large language models from concept to production

A

Paul Iusztin, Maxime Labonne

AI and ML
EN
The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions

The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions

A

Marco Scutari, Mauro Malvestio

AI and ML
EN
Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps

Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps

A

Suhas Pote

AI and ML
RU
Building Machine Learning Applications: From Idea to Product

Building Machine Learning Applications: From Idea to Product

A

Эммануэль Амейзен

AI and ML
RU
Machine Learning System Design

Machine Learning System Design

A

Чип Хьюен (Chip Huyen)

AI and ML
EN
Designing Deep Learning Systems: A Guide for Software Engineers

Designing Deep Learning Systems: A Guide for Software Engineers

A

Chi Wang, Donald Szeto, Yan Xue

AI and ML
EN
MLOps Lifecycle Toolkit: A Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic Systems

MLOps Lifecycle Toolkit: A Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic Systems

A

Dayne Sorvisto

AI and ML
EN
Practical Machine Learning on Databricks

Practical Machine Learning on Databricks

A

Debu Sinha

AI and ML
EN
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

A

Chip Huyen

AI and ML
EN
Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform

Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform

A

Pramod Singh

AI and ML
EN
Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

A

Hannes Hapke, Catherine Nelson

AI and ML

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A Excellent — copyable text, TOC, metadata B Good — text present, some data missing C Average — text is partially extractable D Poor — most likely a scan F Scan without OCR — text cannot be extracted