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July 15-18, 2019
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ML ops: Managing the end-to-end ML lifecycle (sponsored by IBM)
ML Ops day is open to all OSCON 2019 pass holders. (Please note that seating is limited. Please arrive early to ensure seating availability.)
ML Ops day at OSCON is a gathering of industry practitioners discussing production deployments from ML workflows and how to manage them most effectively. Tell us all about the automation and ops tools that you use. From data cleansing all the way to model training, serving, and retraining, ML Ops day at OSCON will tell the story of how to help you do your job with open source tools.
9:00am – 5:00pm Tuesday, July 16, 2019 | Sponsored | Location: E145/146 | Secondary topics: AI Enhanced
Tuesday, July 16, 2019
8:00am
8:00am–9:00am Tuesday, July 16, 2019
Location: Portland Ballroom Foyer and D/E Foyers
Morning Coffee
(1h)
9:00am
Event
Location: E145/146
Paco Nathan (derwen.ai)
Welcome: ML Ops
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9:20am
Average rating:
(5.00, 2 ratings)
Hosting models and productionizing them is a pain point. ML models used for real-time processing require data scientists to have a defined workflow giving them the agility to do self-service seamless deployments to production. Niraj Tank and Sumit Daryani detail open source technologies for building a generic service-based approach for servicing ML decisioning and achieving operational excellence.
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9:55am
Donald Miner (Miner & Kasch)
Average rating:
(4.67, 3 ratings)
Production artificial intelligence systems are interacting with the real world, and it's terrifying that oftentimes nobody has any idea how they're performing on live data. Donald Miner details why you should track your models in production over time, explains how you can implement proper logging and metrics for models, and details metrics you should probably be capturing.
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10:30am
10:30am–11:00am Tuesday, July 16, 2019
Location: Portland Ballroom Foyer and D/E Foyers
Morning Break
(30m)
11:00am
LUCIANO RESENDE (IBM)
Average rating:
(5.00, 1 rating)
The Jupyter Notebook has become the de facto platform for data scientists and AI engineers to build interactive applications and develop AI/ML models. Luciano Resende details how to schedule related notebooks that correspond to different phases of the model lifecycle into notebook-based AI pipelines and walks you through scenarios that demonstrate how to reuse notebooks via parameterization.
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11:45am
Average rating:
(4.67, 3 ratings)
Sophie Watson and William Benton demonstrate high-level open source tools that build on Kubernetes to solve machine learning workflow pain points. They explain why Kubernetes is great for ML and present tools that effortlessly provision custom research environments, publish reproducible notebooks, operationalize models and pipelines as services, and detect data drift automatically.
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12:30pm
12:30pm–1:30pm Tuesday, July 16, 2019
Location: OCC Plaza
Food Truck Lunch
(1h)
1:30pm
Tania Allard (Microsoft)
Average rating:
(5.00, 1 rating)
You're a data scientist interested in improving your workflows, but you've only heard the term DevOps and you don't understand what it is or how to it apply to ML pipelines. Tania Allard guides you through the practical steps to understand and apply DevOps principles and practices to your workflows.
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2:00pm
Average rating:
(4.50, 2 ratings)
GitHub is building a platform for machine learning based on Kubernetes. Michal Jastrzębski and Hamel Husain walk you through an end-to-end project that GitHub open-sourced that automatically labels GitHub issues using machine learning. You'll leave with code and materials so you can replicate everything you've learned.
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2:30pm
Jonathan Peck (GitHub)
Average rating:
(5.00, 1 rating)
ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML.
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3:00pm
3:00pm–3:30pm Tuesday, July 16, 2019
Location: Portland Ballroom Foyer and D/E Foyers
Afternoon Break
(30m)
3:30pm
Saishruthi Swaminathan (IBM),
IH Jhuo (IBM )
Average rating:
(5.00, 1 rating)
Nowadays, AI technologies are pervasive, especially for performance-driven deep learning and microservice is now popular for different applications. Saishruthi Swaminathan and Ih Jhuo guide you through using the microservice and the most recent state-of-the-art AI/deep learning models for various applications via demos.
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4:05pm
Nick Pinckernell (Comcast)
Average rating:
(4.50, 2 ratings)
With ubiquitous ML models, model serving and pipelining is more important now. Comcast runs hundreds of models at scale with Kubernetes and Kubeflow. Together with other popular open source streaming platforms such as Apache Kafka and Redis, Comcast invokes models billions of times per day while maintaining high availability guarantees and quick deployments. Join Nick Pinckernell to learn how.
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