top of page

Learning AutoML

image_edited.jpg

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 book is available on the O'Reilly platform: https://lnkd.in/ev8KD3zR

​

Github repo is here.

Practical Agentic AI

apress.png

Shape the future of AI by engineering agents that think, act, and thrive autonomously. This book connects Agentic AI innovation with production-grade implementation, equipping developers and engineers with the tools and frameworks to deploy AI agents across diverse domains.

 

You'll begin by reviewing the core concepts and principles of Agentic AI, focusing on the key components of autonomous agents such as ReAct and RAG architectures, memory systems, tool orchestration, and interoperability standards.

 

You'll then advance into complex engineering patterns, covering persistent and self-improving agents, multi-agent coordination, and security-compliant deployment strategies—critical for building robust, scalable systems.

 

Looking closely at next-generation capabilities such as cognitive architectures, swarm intelligence, neurosymbolic reasoning, and even quantum-enhanced decision-making, the book uses detailed case studies and complete implementations to help you move from prototypes to production.

 

It also explains the design of agent marketplaces and economic ecosystems, laying the groundwork for interoperable, monetizable AI systems at scale. Domain-specific chapters show how these agents are already transforming finance, healthcare, retail/e-commerce, and scientific research industries.

 

Whether you're building a clinical diagnosis assistant that improves with every patient case or deploying an e-commerce agent that personalizes the customer journey at scale, Practical Agentic AI is your go-to guide. 

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

bottom of page