Presentation: Panel: Predictive Architectures in Practice
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Join a panel of experts and discuss the unique challenges of building and running data architectures for predictions, recommendations and machine learning. Josh Wills built the infrastructure for Slack’s search, learning and intelligence products. Sumit Rangwala is constantly improving People You May Know recommendations for LinkedIn. Eric Chen leads the team that built Michaelangelo, massive scale soup-to-nuts machine learning infrastructure at Uber. Emily Samuels and Anil Muppalla evolved Spotify’s recommendations from batch to real-time. If you ever wondered what it really takes to build data architectures that support large-scale data products, this is the time and place to ask, learn and get inspired.
2019 Tracks
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Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.
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Papers in Production: Modern CS in the Real World
Groundbreaking papers make real-world impact.
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Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.
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Solving Software Engineering Problems with Machine Learning
Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
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Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.
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Predictive Data Pipelines & Architectures
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix
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Sequential Data: Natural Language, Time Series, and Sound
Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.
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ML in Action
Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud
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Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.