Come learn from leaders at Red Hat, Apple, Twitch..

October 17th, 2022 Daniela Lopez de Luise
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Chip Huyen is the CEO of Claypot AI, a platform that leverages the freshest data to make more accurate predictions, get faster insights into the production environment, and speed up model iteration to adapt to data distribution shifts.


Talk: Real-time Machine Learning: Architecture and Challenges


Abstract: Fresh data beats stale data for machine learning applications. This talk discusses the value of fresh data as well as different types of architecture and challenges of online prediction.

What You'll Learn: why fresh data beats stale data for Machine Learning applications.

 
Dr. Anne Martel
Professor in Medical Biophysics at the University of Toronto, the Tory Family Chair in Oncology at Sunnybrook Research Institute, and a Faculty Affiliate at the Vector Institute

Her research program is focused on medical image and digital pathology analysis, particularly on the development of self-supervised and weakly supervised methods for segmentation, diagnosis, and prediction/prognosis.

Talk: Artificial Intelligence And Digital Pathology: Making The Most of Limited Annotated Data

Abstract: Obtaining large datasets with detailed annotations for medical imaging AI projects is a time-consuming and expensive process as it usually requires the input of expert radiologists and pathologists.

This talk will describe several semi-supervised and self-supervised approaches which can make more efficient use of small and/or weakly labelled datasets.

What You'll Learn: Self-supervision and smart sampling strategies are essential in digital pathology
 
Shiming Ren
Sr. Engineering Manager – Safety, MLOps and Infrastructure, Twitch



Talk: From silo to collaboration – building tooling to support distributed ML teams at Twitch


Abstract: In this talk, we will cover Twitch’s current ML team structure and its challenges of it and the solutions we have built to support ML development at Twitch.

We close with a discussion of Twitch’s distributed ML team style and how we collaborate using Conductor as an example.

What You'll Learn: Twitch's strategy of scaling our ML infra and MLOps tooling. The best strategy to utilize ML tooling for enhancing collaborations between ML teams.

This is a good lesson if companies are seeking to start MLOps from scratch.
 
Arthur Vitui
Senior Data Scientist Specialist Solution Architect

Arthur with the help of open-source software is helping organizations develop intelligent application ecosystems and bring them into production using MLOps best practices and is a research assistant in the Software Performance Analysis and Reliability (SPEAR) Lab.

Workshop: Open Source Intelligent Application Delivery on Kubernetes

Abstract: The recent rise in popularity of containerized workloads demanded better ways to orchestrate and manage these workloads hence the creation of the Kubernetes platform.

When it comes to running intelligent application workloads which contain built-in AI/ML software components, the requirement of a Kubernetes platform as a service extends beyond agility, portability, flexibility and scalability as it is required to also answer the data scientist's dilemma: getting started and getting into production.

What You'll Learn: Develop ML models using Jupyter Hub (lab/notebooks) as my preferred research environment, and quick access to resources that support the business logic of my applications, including databases, storage, messaging.
 
 


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