TMLS2020/TMLS2020 Youtube Playlist: Difference between revisions
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== Day 1 == | |||
https://www.youtube.com/playlist?list=PLH-rpi_agJT03iGB8iMgCzcYTXb9JtaZz | https://www.youtube.com/playlist?list=PLH-rpi_agJT03iGB8iMgCzcYTXb9JtaZz | ||
4:39 Your Guide to virtual networking at MLOps Production & Engineering World 2020 | 4:39 Your Guide to virtual networking at MLOps Production & Engineering World 2020 | ||
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== Day 2 == | |||
Revision as of 02:01, 3 December 2020
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
