38:16

Tina White - Decentralized exposure alert protocols for protecting communities from COVID-19

28:57

Dr. Benjamin Fine & Liam McCoy - HowsMyFlattening: Ontario's COVID-19 Data Analytics Collaborative

28:30

TMLS2019 - Laila Paszti

43:33

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

23:44

TMLS2019 - Satya Krishna Gorti

32:02

Sheldon Fernandez - COVID-Net for COVID detection and risk stratification via chest radiography

29:02

Stephen Mackinnon - PolypharmDB, Quickly Identifies Repurposed Drug Candidates for COVID-19

48:30

Machine Learning Simplified from Ideation to Deployment in Minutes with Automated Machine

46:15

Sabina Stanescu - Your First ML Model In Production: Examples & Considerations

19:43

Parnian Afshar - Capsule Network-based Framework for Identification of COVID cases from X-ray Images

23:42

TMLS - 2019 Sheldon & Michael

46:03

Kaushik Roy - Are Your Models Location Smart?

33:10

Vinnie Saini - RPA and AI disrupting the Financial Industry

30:27

Dmitry Baev - Deploying Distributed AI and Machine Learning in Financial Services

28:21

Dr. Neeraj Kashyap - Machine Learning

31:18

TMLS2019 - Jaya Kawale

01:16:22

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

35:22

Dean Wampler - Ray and how it enables easier DevOps

34:20

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

47:39

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

01:03:55

Yaron Haviv - Simplify ML Pipeline Automation and Tracking using Kubeflow and Serverless Functions

45:47

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

28:26

Ayesha Hafeez - Walkthrough: Utterance Generator Fast Tracks Chatbot Training

39:26

Abe Gong - Fighting Pipeline Debt With Great Expectations

01:48:43

Josh Poduska - Workshop: Turbo-Charging Data Science with AutoML

19:49

Innodata Presents: Bogged Down by Annotation Why SMEs Should Do the Heavy Lifting

24:41

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

01:54:46

Josh Mineroff - Workshop: Intro to ML with Python

52:11

Bahareh, Eric and Safura - Workshop: Trusted AI: a workshop on IBM Watson OpenScale

45:34

Don Ward - Edge AI - The Next Frontier

51:45

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

01:02:19

Moez Ali - Workshop: Machine Learning with PyCaret

27:04

Subhodeep Moitra - Deep Learning for Program Repair

38:37

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

43:20

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

49:30

Alexander Wong - Workshop: How a human-machine collaboration approach to deep learning development

43:49

Hamza Tahir - Why ML in production is STILL broken?

01:02:47

Srikar Kovvali - Workshop: Post-COVID Supply Chain MLOps Model Re-Training

33:07

Lina Weichbrodt - How To Monitor Machine Learning Stacks

48:22

Boris Lublinsky - Using Model Serving in Streaming Applications

57:55

How to automate machine learning with GitHub Actions

48:11

Olivier Blais - Validate and Monitor Your AI and Machine Learning Models

37:06

Xiaoming Zhang - Productionizing ML Models at Online Shopping at Loblaws

48:31

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

53:52

Industry Survey Analysis: The industry Landscape of Natural Language Use Cases in 2020

49:41

Abstraction and Analogy in Natural and Artificial Intelligence

02:01:10

MLOps & Automation Workshop: Bringing ML to Production in a Few Easy Steps

32:27

Autonomous Vehicles-The Next Step Forward

01:01:20

Vin Vashishta - Now What Machine Learning After COVID

47:00

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

18:09

Human Interpretable Machine Learning for Smart Water Management

01:26:20

Build, Train, and Deploy models with Amazon SageMaker

45:01

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

40:23

Docker Based Workflow for Deploying a Machine Learning Model

01:40:30

Managing Data Science in the Enterprise

01:29:20

Building a MovieLens Recommender System

01:19:03

Reaching Lightspeed Data Science: ETL, ML, and Graph with NVIDIA RAPIDS

46:08

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

02:15:08

Computer Vision in Practice - Building an End-to-End Pipeline for Object Detection and Segmentation

01:02:18

Knowledge Graph Recommendation Systems for COVID 19