Getting Started in Machine Learning

Machine Learning powers much of what we see and interact with. Get to grips with the theoretical and practical considerations in ML and understand the tools and platforms that make up the discipline

From this track


Recommender and Search Ranking Systems in Large Scale Real World Applications

Recommendation and search systems are two of the key applications of machine learning models in industry. Current state of the art approaches have evolved from tree based ensembles models to large deep learning models within the last few years.

Speaker image - Moumita Bhattacharya
Moumita Bhattacharya

Senior Research Scientist @Netflix


Verifiable and Navigable LLMs with Knowledge Graphs

Graphs, especially knowledge graphs, are powerful tools for structuring data into interconnected networks. The structured format of knowledge graphs enhances the performance of LLM-based systems by improving information retrieval and ensuring the use of reliable sources.

Speaker image - Leann Chen
Leann Chen

AI Developer Advocate @Diffbot


No More Spray and Pray— Let's Talk About LLM Evals

The pace of development in AI in the past year or so has been dizzying, to say the least, with new models and techniques emerging weekly. Yet, amidst the hype, a sobering reality emerges: much of these advancements lack robust empirical evidence.

Speaker image - Apoorva Joshi
Apoorva Joshi

AI Developer Advocate @MongoDB

Track Host

Susan Shu Chang

Principal Data Scientist @Elastic, Author of "Machine Learning Interviews"

Susan Shu Chang is a principal data scientist at Elastic, which powers search around the world. Previously, she built machine learning at scale in the fintech, social, and telecom industries. She is the author of Machine Learning Interviews, published by O’Reilly.

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