During his presentation, Norbert will demonstrate a use case on leveraging language models to support sourcing and SEO optimization decisions for e-commerce companies.

Who is it for?
Technically affine people who are interested in seeing the journey of solving a use case with ML from ideation till deployment.

What will you learn?
Tips and tricks using language models on search keywords. Real-life challenges and solutions including transformers, clustering, streamlit, GCP.

Real-world machine learning projects use staggering amounts of data, the processing of which raises both spatial and temporal concerns. In this talk, participants will learn about the different theoretical approaches to solve the handling of large amounts of data and efficient computational methods, including GPU usage and Google Cloud tools to not simply enhance, but make possible certain workflows.

Who is it for?
Data scientists or Python programmers who have to work with either large datasets or perform expensive computations; those interested in the preceding.

What will you learn?
Theoretical solutions, including task graphs, lazy evaluation, and disk spillover. Practical examples, including libraries for efficient and distributed computations and GPU-based workflows. Industry use cases, including GCP and Kubeflow based pipelines.