Presentation: On a Deep Journey Towards Five Nines
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Watch video with transcriptAbstract
At PayPal, achieving four nines of availability is the norm. In the pursuit of exponentially complex additional nines, the company has recently embarked on applying deep learning to forecasting datacenter metrics. Seq2Seq networks are ripe for application to this difficult problem, but little has been shared to the open community.
Aashish Sheshadri shines a light on how PayPal applies Seq2Seq networks to forecasting CPU and memory metrics at scale. Forecasting enables alerting flows to get a head start reducing MTTD, augment autoremidiation, and consequentially aid MTTR. In doing so Aashish describes the ecosystem and tooling that enables developers at PayPal to experiment, build and train ML models while creating reusable, reproducible and sharable work in the Jupiter and Kubernetes ecosystem.
2019 Tracks
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Handling Sequential Data Like an Expert / ML Applied to Operations
Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.
Half-day tracks -
AI Meets the Physical World
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Hands-on Codelabs & Speakers Office Hours
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Papers in Production: Modern CS in the Real World
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Hands-on Codelabs & Speakers Office Hours
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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
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