Automating your developer workflow on GitHub with TensorFlow
Who is this presentation for?
- ML engineers, data scientists, and DevOps engineers
Software development is central to machine learning, regardless of if you’re prototyping in a Jupyter notebook or building a service for millions of users. One of the promises of machine learning is to automate mundane tasks and augment our capabilities, making us all more productive. However, one domain that doesn’t get much attention that’s ripe for more automation is the domain of software development itself.
Hamel Husain, Omoju Miller, Michał Jastrzębski, and Jeremy Lewi explain how to use publicly available datasets and machine learning to automate developer workflows for yourself and all your friends using GitHub apps. You’ll step through a “Hello, world” example using TensorFlow to build an app that’s already in production and used by several popular open source projects. Additionally, you’ll get resources and ideas you can use to build other products and explore how these products can be monetized. Hamel, Omoju, Michał, and Jeremy detail how to optionally leverage Kubeflow and Kubernetes to deploy these models at scale.
- General knowledge of deep learning (useful but not required)
What you'll learn
- Learn how to use a freely available natural language dataset to build practical applications
Hamel Husain is a data scientist at GitHub who is focused on creating the next generation of developer tools powered by machine learning. His work involves extensive use of natural language and deep learning techniques to extract features from code and text. Previously, Hamel was a data scientist at Airbnb, where he worked on growth marketing, and at DataRobot, where he helped build automated machine learning tools for data scientists. Hamel can be reached on Twitter.
Omoju Miller is a machine learning engineer with GitHub. Previously, she co-led the nonprofit investment in computer science education for Google and served as a volunteer advisor to the Obama administration’s White House Presidential Innovation Fellows. She’s a member of the World Economic Forum Expert Network in AI.
Michał Jastrzębski is staff data engineer at GitHub, where he builds machine learning infrastructure for internal use. Previously, he was an architect at Intel’s Open Source Technology Center. Michał has a long experience in cloud technologies like OpenStack and Kubernetes, both as an operator and contributor. As former leader of OpenStack Kolla, he managed a community of more than 200 people and almost 40 companies. Michal has been involved with machine learning on Kubernetes communities like Kubeflow.
Jeremy Lewi is a cofounder and lead engineer for the Kubeflow project at Google—an effort to help developers and enterprises deploy and use ML cloud natively everywhere. He’s been building on Kubernetes since its inception, starting with Dataflow and then moving onto Cloud ML Engine and now Kubeflow.
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