Track

ML Everywhere

ML is pervasive and affects different verticals in the economy, from sales operations, to forecasting applications, marketing campaigns, and healthcare systems. ML is really transforming the world around us, creating an avenue to innovation across all sectors of the global economy. In this track you will learn all about the latest ML innovations and the multiple fields where and how ML is being applied and deployed: in particular, you will learn about ML operations (MLOps) to accelerate AI adoption and the latest open-source frameworks for improving real-world ML applications.


From this track

Session

What You Should Know Before Deploying ML in Production

Tuesday Nov 9 / 08:00AM PST

MLOps provides tools that make building, deploying, and maintaining machine learning solutions easier than ever before. However, MLOps is not only a static set of tools that defines the way you operationalize your machine learning models, but is also (and most importantly) about your organization...

Francesca Lazzeri

Principal Data Scientist Manager @Microsoft

Session

Continual Learning for Machine Learning in Production

Tuesday Nov 9 / 09:00AM PST

Because of data drift, the accuracy of an ML model degrades over time. How well a model performs depends on how often it’s updated. While companies like Alibaba, ByteDance, Google, Facebook have been able to leverage real-time pipelines to continually update many of their models in...

Chip Huyen

Founder at stealth startup & Teaching ML Sys @Stanford

Session

Machine Learning

Tuesday Nov 9 / 10:00AM PST

The session details are currently being prepared.

Session

Machine Learning Panel

Tuesday Nov 9 / 11:00AM PST

The session details will be updated soon.

Date

Tuesday Nov 9 / 08:00AM PST

Topics

Machine Learning

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Track Host

Francesca Lazzeri

Principal Data Scientist Manager @Microsoft

Francesca Lazzeri, PhD is an experienced scientist and machine learning practitioner with over 12 years of both academic and industry experience. She is author of the book “Machine Learning for Time Series Forecasting with Python” (Wiley) and many other publications, including...

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