Join Kirill Eremenko, CEO of Super Data Science, and Hadelin de Ponteves, a leading AI educator, as they delve into the transformative power of foundation models for businesses. They discuss the eight-step lifecycle for implementing these models, offer criteria for selecting the best fit, and explore clever customization techniques. The duo also introduces AWS generative AI tools, making it easier for companies to leverage AI without breaking the bank. Their insights are a treasure trove for anyone looking to navigate the generative AI landscape!

Machine learning expert Kirill Eremenko discusses decision trees, random forests, and the top gradient boosting algorithms: XGBoost, LightGBM, and CatBoost. Topics include advantages of XGBoost, LightGBM efficiency, and CatBoost's handling of categorical variables.

Kirill Eremenko, CEO of Super Data Science, shares six key insights on data science careers, emphasizing practical skills, strong portfolios, mentorship, and collaboration. He discusses the importance of incorporating machine learning into web apps for enhancing employability and highlights the diverse backgrounds of remote-working data scientists.

In this podcast, Kirill Eremenko, the SuperDataScience founder, discusses full encoder-decoder transformers with Jon Krohn. They cover topics like how cross-attention works, the importance of masking during self-attention, and the collaboration dynamics in transformer research. The episode provides a detailed explanation of encoder-decoder transformers, language models, and the use of transformers in natural language processing.

Data scientist Kirill Eremenko discusses the basics of transformers and LLMs, emphasizing the five building blocks of transformer architecture and why transformers are so powerful. Topics include AI recruitment, a new course on LLMs, and the impact of LLMs on data science jobs.