Alejandro Saucedo

This talk is an exciting journey that revisits the past decade of Production Machine Learning from 2015 until now, and provides a pragmatic outlook of the next decade towards 2035.

As the number of production machine learning use-cases increase, we find ourselves facing new and bigger challenges where more is at stake. Because of this, it's critical to identify the key areas to focus our efforts, so we can ensure our machine learning pipelines are reliable and scalable.

As data science capabilities scale, the core concept of security becomes growingly critical - in this talk we provide an overview of challenges, solutions and best practices to introduce security into the ML lifecycle.

As data science capabilities scale, the core concept of security becomes growingly critical - in this talk we provide an overview of challenges, solutions and best practices to introduce security into the ML lifecycle.

Many advanced data processing paradigms fit incredibly well to the parallel-architecture that GPU computing offers, which has resulted in the continuously growing adoption of graphics card for general purpose computing. Exciting advancements in the open source Vulkan Project are enabling developers to take advantage of general purpose GPU computing capabilities in cross-vendor mobile and desktop…

Undesired bias in machine learning has become a worrying topic due to the numerous high profile incidents. In this talk we demystify machine learning bias through a hands-on example.

Description This talk will provide a practical overview of the state of production machine learning frameworks, tools and techniques that seem to have become a trend for the coming year. I will be mainly covering the open source tools and frameworks available in 2019 to help you expand your DataOps and MLOps infrastructure.