TMLS2020/TMLS2020 Youtube Playlist

Workshops

FIXME

Day 1

https://www.youtube.com/playlist?list=PLH-rpi_agJT03iGB8iMgCzcYTXb9JtaZz

4:39 Your Guide to virtual networking at MLOps Production & Engineering World 2020

48:22 Boris Lublinsky - Using Model Serving in Streaming Applications

1:16:22 Denise Gosnell -Modeling, Querying and Seeing Time Series Data within a Self-Organizing Mesh Network

43:49 Hamza Tahir - Why ML in production is STILL broken?

51:45 AI to AEYE: See the Value of AI as Investor & 5 Key Factors That Can Attract Investors to AI company

1:05:43 Mark McQuade and Tanya Vucetic - Automating Production Level Machine Learning Operations on AWS

50:02 Patrick Hall - Real-World Strategies for Model Debugging

1:03:55 Yaron Haviv - Simplify ML Pipeline Automation and Tracking using Kubeflow and Serverless Functions

24:41 Lina Palianytsia - Metrics: Holistic Health Metrics of ML-Based Product

47:56 Nick Pogrebnyakov - Implementing a ML Initiative: A Leader's Perspective

32:44 Chanchal Chatterjee - Quickly Deploy ML Workloads on Multi-Cloud Using Kubeflow Pipelines

27:04 Subhodeep Moitra - Deep Learning for Program Repair

33:07 Lina Weichbrodt - How To Monitor Machine Learning Stacks

43:33 Kenny Daniel - DevOps for ML and other Half-Truths: Processes and Tools for the ML Lifecycle

43:20 Jan Zawadzki - The Do’s and Don’ts of Delivering AI Projects: A Practitioners Guide

36:13 Brandy Freitas - Team Roles in a Machine Learning Project and Project Flow

1:04:38 Women in AI: Transitioning Careers into AI, Challenges, Opportunities from a Female Perspective

45:13 Jon Peck - GitHub Actions in Action

44:49 Luna Feng - Create Harmony Between ML Engineers and Researchers

46:11 John Peach - Literate Statistical Programming

1:01:20 Vin Vashishta - Now What Machine Learning After COVID

47:00 Stacey Svetlichnaya - Hyperparmeter Tuning With a Focus on Weights & Biases Sweeps

46:08 Sabina Stanescu - Your First ML model in Production Considerations & Examples

45:47 Ebrahim & Jisheng - Automated Pipeline for Large-Scale Neural Network Training and Inference

Day 2

44:08 Sharat Singh - Engineering and Production Techniques for Managing Feature d

38:51 David Talby - Lessons Learned Building Natural Language Processing Systems

45:01 Yiannis Kanellopoulos - How F.A.T is your ML Model Quality in the era of Software

42:44 Maximo & Javier - Smart Data Products: From prototype to production

51:45 AI to AEYE: See the Value of AI as Investor & 5 Key Factors That Can Attract Investors to AI company

48:31 Ira Cohen - ML monitoring ML: Scalable monitoring of ML models in production environments

35:22 Dean Wampler - Ray and how it enables easier DevOps

38:41 Patricia Thaine - Privacy-Preserving Machine Learning

37:06 Xiaoming Zhang - Productionizing ML Models at Online Shopping at Loblaws

47:39 LTC Isaac J Faber Ph D - Building an AI Capability in the United States Army

34:20 Saranyan - MLOps that works: How we built ML pipelines for deploying models for autonomous factories

48:11 Olivier Blais - Validate and Monitor Your AI and Machine Learning Models

33:06 Muna Khayyat - Machine Learning in Finance, Best Practices and Insights

46:03 Kaushik Roy - Are Your Models Location Smart?

36:41 Ramzi Abdelmoula -How to set your internal AI initiative for production-level success

33:41 Elle O'Brien - Adapting continuous integration and continuous delivery for ML

45:34 Don Ward - Edge AI - The Next Frontier

56:15 Brooke Wenig and Jules Damji - Managing Machine Learning Experiments with MLflow

38:37 Alice and Hubert - MLOps at Scale: Predicting Bus Departure Times using 18,000 ML Models

39:26 Abe Gong - Fighting Pipeline Debt With Great Expectations