Computer Vision
NLP
Deep Learning
Machine Learning
AutoML
MLOps
| 8:00am | Check In | |||
| 9:00am Keynote | Simulations – The New Reality of AI Danny Lange, VP of AI, Unity | |||
| 9:40am Keynote | Patterns & Practices – Lessons Learned from Azure ML Customers Erez Barak, Sr. Director, Microsoft AI | |||
| 10:20am | Coffee break and networking | |||
| 10:45am Keynote | AI to Empower Agent Intelligence Joseph Sirosh, CTO, Compass | |||
| 11:25am Keynote | Autonomous ML: Plug and Play AI Bindu Reddy, CEO, RealityEngines.ai | |||
| 12:00pm | Lunch Break and Networking (407&408) | |||
1:00pm - 1:50pm | ||||
| 406 | Developing and Troubleshooting Neural Networks Cibele Montez Halasz, Apple | |||
| 404 | Open-Ended Reinforcement Learning Rui Wang, Uber AI | |||
| 403 | Machine Learning Platform at Twitter Redwan Rahman, Twitter | |||
| 402 | Multivariate Content Optimization: Real-time Recommendation and Business Insights Yi Liu, Amazon | |||
2:00pm - 2:50pm | ||||
| 406 | Conversation Intelligence: Deep Learning for Signal Detection in Business Calls William Li, Marchex | |||
| 404 | Getting Data Ready for Data Science with Delta Lake Denny Lee, Databricks | |||
| 403 | Monitoring of Machine Learning Models Aalok Shanbhag, Fiddler Labs | |||
| 402 | Scalable Automatic Machine Learning Michelle Tanco, H2O.ai | |||
| 2:50pm | Coffee Break and Networking | |||
3:10pm - 4:00pm | ||||
| 406 | Practical Reinforcement Learning via the Azure Personalizer Service Paul Mineiro, Microsoft | |||
| 404 | Taking Machine Learning Research to Production: Solving Real Problems Anusha Ramesh, Google AI | |||
| 403 | Modern Speech Recognition: Hybrid vs. End-to-End Duc Le, Facebook AI | |||
4:10pm - 5:00pm | ||||
| 406 | Bridging Functions and Functionality -- How To Empower Data Scientists To Do More, By Doing Less Elijah ben Izzy, Stitch Fix | |||
| 404 | CPU and Accel Architectures and Their Performance at Scale for ML and DL Meena Arunachalam, Intel | |||
| 403 | On-device ML with TensorFlow Lite Margaret Maynard-Reid, ML Google Developer Experts | |||
5:00pm - 7:30pm: Evening Session | ||||
| Reception | Speakers, Invited Guests, and Conferenceplus/Workshop/Training ticket holders only. | |||
| 9:00am | Check In | |||
9:30am - 10:20am | ||||
| 404 | Continuous Delivery and Automation Pipelines in Machine Learning Valliappa Lakshmanan(Lak), Google | |||
| 405 | Sequential Recommendations with Structured Long-term Dependencies Chong Wang, ByteDance | |||
| 406 | Accelerating Computer Vision Models with TensorFlow on FPGAs Ted Way, Microsoft | |||
10:40am - 11:30am | ||||
| 404 | Building a Predictive Maintenance System using IoT data Zhen Li, Microsoft | |||
| 405 | ML CI/CD: Running Abstract Machine Learning Frameworks Inside GitHub Actions Jon Peck, Github | |||
| 406 | Optimization Algorithms for Improving The Product Experience Milind Kopikare, Qualtrics | |||
| 12:00pm | Lunch break and networking (407&408) | |||
1:00pm - 1:50pm | ||||
| 404 | DeepFake Detection Challenges Brian Dolhansky, Facebook | |||
| 405 | Prototype to Production: How to Scale your Deep Learning Model Swetha Mandava, Nvidia | |||
| 406 | Building Real Time Cross Platform Video Audio ML Pipelines Hadon Nash, Google Research | |||
2:00pm - 2:50pm | ||||
| 404 | Introduction to Kubeflow and Pipelines Amy Unruh, Google | |||
| 405 | Interpretable Machine Learning Models Soma Bhattacharya, Expedia Group | |||
| 406 | Time Series Forecasting from Statistical Methods to Deep Learning Yuan Shen, Oneclick.ai | |||
9am - 12pm | |
| Track 1 | Serverless Machine Learning with TensorFlow 2.0 (1) by Google Team Lectures, live demo, and hands-on code labs. Workshop Details |
| 12pm-1pm | lunch break |
1pm - 4pm | |
| Track 1 | Serverless Machine Learning with TensorFlow 2.0 (2) by Google Team Lectures, live demo, and hands-on code labs. Workshop Details |
9am - 12pm | |
| Track 1 | Deep Learning for NLP by Zhen Li, Microsoft Lectures, live demo, and hands-on code labs. Workshop Details |
| 12pm-1:00pm | lunch break and networking |
1:00pm - 2:30pm | |
| Track 1 | Deep Learning for NLP (continue) by Zhen Li, Microsoft Lectures, live demo, and hands-on code labs. Workshop Details |
9am - 4pm | |
| Track 1 | Deep Learning for Computer Vision by Andrew Ferlitsch, Google *This 2-day immersive instructor-led training will teach everything you need to know to become a software engineer in deep learning and computer vision. Course Syllabus |
50+ tech lead speakers from Engineering Teams at Microsoft, Google, Amazon, Facebook, Uber, Linkedin, Pinterest, Nvidia, Twitter, and more.
60+ deep dive tech topics and practicial experiences in machine learning, deep learning, computer vision, speech reconginition, NLP, data science and analytics. specially geared to tech engineers who want to grasp AI tech applied to their daily project.
Connect with 500+ tech engineers, developers, data scientists; learn from peers, small-group discussions, office-hour, and lunch with speakers, happy hours.
Continue to learn and practice AI post conference, join our free online AI learning group with 400+ tech speakers, 85,000+ tech engineers. Learn more.
The speakers and sponsors teams are hiring tech engineers, developers, data scientitst, machine learning engineers and algorithm engineers. Come to talk and connect to the hiring manager and tech lead of the teams.
