#MLOpsNYC19
AGENDA
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8:00-9:00
Registration and exhibitions
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9:00-9:30
Keynote: MLOps in the Newsroom
Chris Wiggins, Chief Data Scientist, The New York Times
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9:30-10:00
Using MLOps to Bring ML to Production
David Aronchick, Head of Open Source ML Strategy, Microsoft
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10:00-10:30
Netflix Presents: A Human-Friendly Approach to MLOps
Julie Pitt, Director, Data Science Platform, Netflix
Ashish Rastogi, Content Machine Learning Lead, Netflix
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10:30-10:50
Break
10:50-11:20
Data as the Enabler of Digital Transformation
Bill Groves, Chief Data Officer, Walmart
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11:20-11:50
Real-time Financial Fraud Detection
Arthur Garmider, Architect, Payoneer
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Panel: Practical ML Challenges
Josh Baer, Machine Learning Platform Product Lead, Spotify
Jason Evans, Director of DXP Innovation, Quadient
Michael Skarlinski, Manager of Data Science, WW
Kishore Gagrani, Global Product Director, Dell PowerEdge
Moderated by Asaf Somekh, Iguazio CEO
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12:35-1:35pm
Lunch
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1:35-2:05pm
The Architecture That Powers Twitter’s Feature Store
Brittany Wills, Software Engineer, Twitter
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2:05-2:35pm
Serverless for ML Pipelines from A to Z
Orit Nissan-Messing, VP of R&D, Iguazio
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Deep Learning on Business Data at Uber
Alex Sergeev, Software Engineer, Uber
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3:05-3:35pm
The Growth and Future of Kubeflow for ML
Maulin Patal, Product Manager, Google
Jeremy Lewi, Lead Kubeflow Engineer, Google
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3:35-3:55pm
Break
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3:55-4:25pm
Stateless ML Pipelines
James Norman, Lead Software Engineer, Nike
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4:25-4:55pm
The RAPIDS Ecosystem – Scaling Accelerated Data Science
Josh Patterson, GM, Data Science, NVIDIA
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Best Practices for Multiplatform MLOps with Kubeflow and MLflow
Clemens Mewald, Director of Product Management, Machine Learning and Data Science, Databricks
David Aronchick, Head of Open Source Machine Learning Strategy, Microsoft
Thea Lamkin, Open Source Program Manager, Google
Moderated by Yaron Haviv, CTO, Iguazio
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5:40-6:00pm
MLOps Challenges and Future
Yaron Haviv, CTO, Iguazio
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6:00-7:00pm
Exhibition
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6:00-8:00pm
MLOps Rooftop Drinks
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8:00-9:00
Registration and exhibitions
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9:00-10:30
Building an End-to-End Machine Learning Pipeline with Kubeflow
Karl Weinmeister, Developer Advocacy Manager, Google
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10:30-10:50
Break
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10:50-11:35
Deploying Serverless ML pipelines with MLRun, Nuclio and Kubeflow
Or Zilberman, Data Scientist, Iguazio
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11:35-12:35pm
Accelerating Machine Learning with MLflow
Ben Wilson, Senior Resident Solutions Architect, Databricks
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12:35-1:35
Lunch
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1:35-2:05pm
Running Spark on Kubernetes
Marcelo Litovsky, Solutions Architect, Iguazio
Adi Hirschtein, VP of Product, Iguazio
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Spotify's Machine Learning Workflow
Ryan Clough, Senior Machine Learning Engineer, Spotify
Gandalf Hernandez, Machine Learning Platform Engineering Manager, Spotify
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2:50-3:35pm
Introducing KFServing: Serverless Model Serving Across ML Frameworks
Dan Sun, Senior Software Developer, Bloomberg
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3:35-3:55pm
Break
3:55-4:25pm
Kubeflow 0.6 Release Update
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4:25-4:55pm
Steven Jones, Lead Architect Messaging and AI, IBM
Jenny Mallette, Cognitive Software Engineer, IBM
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4:55-5:25pm
Machine Learning with SageMaker
Mark Roy, Machine Learning Specialist, AWS
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5:25-6:00pm