# 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.

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## 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.

## 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.

## Specifications

### Publication details
- Publisher: O'Reilly Media
- Publication date: May 12, 2026
- Edition: 1st
- Language: English
- Print length: 590 pages
- ISBN-13: 979-8341643185
### Dimensions and installation
- Dimensions: 7 x 2 x 9.19 inches
- Weight listed: 2.05 pounds

## 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.

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## Images

![Learning AutoML: AutoGluon and ML Pipeline Automation — 1](https://best-deals.pro/i/oDaQUIgQgboOU0xZrc2x_AEMMClAt2-9jyCUREBsj6T3TOael-BudZEtsVxdm10qhOIscU-ydJ2TbNLu7XRShEBJqPTWcoJm8Rb4XoqCbdcFbTb6_JOQ.jpg)