Vinod Chugani was born in India and raised in Japan, and brings a global perspective to data science and machine learning education. He bridges the gap between emerging AI technologies and practical implementation for working professionals. Vinod focuses on creating accessible learning pathways for complex topics like agentic AI, performance optimization, and AI engineering. He focuses on practical machine learning implementations and mentoring the next generation of data professionals through live sessions and personalized guidance.
A comprehensive guide to building AI systems that can plan, reason, and act autonomously — from basic tool-using agents to sophisticated multi-agent collaborations.
Learn how to migrate from Pandas to Polars with practical examples, side-by-side code comparisons, and strategies to unlock performance improvements on your existing data workflows.
A practical roadmap for Python programmers to develop the advanced skills, specialized knowledge, and engineering mindset needed to become successful AI engineers in 2025.