Virtual Event Agenda
July 17, 2021 9AM - 1:00PM PDT
Sessions will be recorded and uploaded to Youtube
ML Models in Image Search: Selecting for the User
9:15am - 9:45am PDT
Director of ML/AI @ Getty Images
Image search relies on many different types of data – some sourced from image creators, some developed by machine learning models, some extracted from the surrounding image context. Different data is optimal for different search models, but what data is best for the user? This talk will discuss leveraging machine learning in image search, while keeping the user top of mind.
NLP at Scale
9:45am - 10:15am PDT
VP Engineering, Head of Search and Conversational AI @ Zillow
A deep dive into industrial applications of NLP and best practices for building NLP systems
Training Models at Scale on the Cloud with Grid.ai
10:20am - 10:50am PDT
CEO & Founder @ Grid.ai
Creator of PyTorch Lightning
This session is about how to train models on the cloud without changing a single line of code. From model development to hyperparameter sweeps, Grid automates all the engineering, so you can focus on machine learning instead of getting bogged down in infrastructure bottlenecks. In this session, William will walk you through the general workflow of research, from creating a hypothesis on a model on jupyterlab or on SSH to training a large-scale network using multiple GPUs simultaneously without having to change a single line of code.
Graph Data Science: What’s the Big Deal?
11:30am - 11:55am PDT
Director of Data Science @ Neo4J
By now, you've probably heard about graphs and data science – whether from a Gartner top trends report or an academic paper on the latest neural network or AI/ ML techniques. But what is graph data science, and why should you care? In this talk we'll explain how graph technology allows you to build more accurate predictive models and answer questions you couldn’t otherwise by analyzing the relationships between your data. We'll also cover common use cases for graphs in data science and how Neo4j helps you operationalize graph data science at scale.