Learning AutoML
Learning AutoML captures my journey in building ML applications in various settings, utilizing autogluon package as a tool to demonstrate different dimensions of building an end-to-end solution with AutoML.
The book covers:
- Building AutoML pipelines for tabular, text, image, and time series data
- Deploying models with fast, scalable workflows using MLOps best practices
- Comparing and navigating today's leading AutoML platforms
- Interpreting model results and making informed decisions with explainability tools
- Exploring how AutoML leads into next-gen agentic AI systems
Learning AutoML is scheduled for late 2025. An early draft of the first two chapters is available on the O'Reilly platform: https://lnkd.in/ev8KD3zR
Media Mentions
With generative AI, anyone can code. Here’s how to help your enterprise embrace this change. by Thomas H. Davenport, Ian Barkin and Kerem Tomak
His strategic approach focused on building both "offensive" and "defensive" capabilities, resulting in innovative solutions ranging from automated credit processing systems and sophisticated fraud detection algorithms to comprehensive client intelligence platforms and dynamic pricing optimization tools.
“A lot of times we think of digital transformation as a technology dependent process. The transformation takes place when employees learn new skills, change their mindset and adopt new ways of working towards the end goal.”–Kerem Tomak