Learning AutoML: AutoGluon and ML Pipeline Automation
Pipeline Automation: Learning AutoML is a practical guide to automated machine learning centered on AutoGluon, with coverage of model workflows, deployment, and other AutoML platforms. It is presented for readers with basic data-analysis familiarity, from practitioners seeking technical detail to business and domain readers seeking applied context.
At a glance
- Uses AutoGluon as the primary toolkit while discussing Auto-sklearn, H2O.ai, Vertex AI, SageMaker Autopilot, and Azure AutoML.
- Describes workflows for tabular, text, image, and time-series data.
- Covers model evaluation, explainability, deployment, CI/CD, and MLOps integrations including Kubeflow, MLflow, and Airflow.
- The listing describes Python examples and says the material assumes basic familiarity with data analysis, not deep machine-learning expertise.
Reference price: $79.99
Price checked: Nov 14, 2026
View offerAhead of Black FridayExplore the selectionPros and cons
Pros
- The stated scope bridges model building and production integration rather than focusing only on experimentation.
- Coverage of multiple data types and named platforms can help readers compare approaches across projects.
- The listing identifies audiences ranging from data scientists and engineers to analysts, students, and decision-makers.
Technical specifications
8 specifications| Publisher | O'Reilly Media |
|---|---|
| Publication date | May 12, 2026 |
| Edition | 1st |
| Language | English |
| Print length | 590 pages |
| ISBN-13 | 979-8341643185 |
| Dimensions | 7 x 2 x 9.19 inches |
|---|---|
| Weight listed | 2.05 pounds |
Who is it for?
A good fit for
- Data scientists and ML engineers exploring automated workflows and deployment.
- Analysts and domain experts who want practical AutoGluon examples with limited programming experience.
- Software developers integrating machine-learning models into applications.
- Students, educators, consultants, and technical decision-makers seeking an overview of AutoML.
Frequently asked questions
What topics does Learning AutoML cover?
The listing describes AutoGluon-based model building for tabular, text, image, and time-series data, plus platform comparisons, evaluation, explainability, deployment, and MLOps.
Does the book require advanced machine-learning expertise?
The listing says it assumes basic familiarity with data analysis but does not require deep machine-learning expertise. It describes intuitive explanations and Python examples.
Who may find this book useful?
The stated audiences include data scientists, ML engineers, analysts, software developers, students, educators, consultants, and business decision-makers.
Which tools and platforms are discussed?
The listing names AutoGluon, Kubeflow, MLflow, Airflow, Vertex AI, SageMaker Autopilot, Azure AutoML, Auto-sklearn, and H2O.ai.
Alternative options
Time Series with PyTorch: Modern Deep Learning Toolkit for Real-World Forecasting Challenges
$39.99
LLM Engineer's Handbook: Master the art of engineering large language models from concept to production
$35.23
Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents
$41.99
Unlocking Data with Generative AI and RAG: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall
$37.99
LLMs for Modern Software Delivery and DevOps: Applying Large Language Models to Software Delivery and SRE
$28.99
Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based agentic AI framework with tool use, memory, and multi-agent workflows
$38.49
The Cloud FinOps Playbook for Google Cloud: A practical guide to adopt, build, and scale Cloud FinOps
$28.99
Python Programming for Beginners: The Ultimate Beginner's Guide to Learn Python Programming Step-by-Step (Computer Programming)
$35.00
The Tradeoff Method for ML Systems: Machine learning and GenAI system design interviews
$25.00
Machine Learning Hero: Master Data Science with Python Essentials: Machine Learning with Python Hands-On Guide from Beginner to Expert (Mastering the AI Revolution)
$49.90
Python Become a Master: 120 ‘Real World’ Python Exercises with more than 220 Concepts Explained: Practice Makes a Master. Real python projects to code ... From Basics to Real-World Applications)
$37.12
Learn to Create Machine Learning Models: Create your professional portfolio of Machine Learning Models (AI - Learn then Implement Book 3)
$7.99
