AI Disruptors: A Selection of Case Studies

Name: AI Disruptors: A Selection of Case Studies
Start: 2023-04-13T09:00:00-07:00
End: 2023-04-13T10:00:00-07:00

Virtual

Apr 13, 2023, 4:00 – 5:00 PM (UTC)

Virtual event

AI and Machine Learning

Speakers

Bonnie Donovan

Google Cloud

Head of Data Provider Business Development on, Data Partner Ecosystem

Karl Schutz

Snorkel AI

Enterprise Account Executive

Mona Rakibe

Telmai

Co-Founder & CEO

Phi Nguyen

Anyscale

Technical Product Marketing Manager

Kevin Stumpf

Co-Founder and CTO

Tecton, Inc.

When

Thursday, April 13, 2023
4:00 PM – 5:00 PM (UTC)

Agenda

Welcome and Intro	Bonnie Donovan, Head of Ecosystem Growth & Partnership Management, Google Cloud
Tecton	In this session, we will provide an overview of the Tecton Feature Platform. We will talk about the different components of the platform that enable organizations across industries to infuse real-time machine learning decision-making into their customer facing applications.
Anyscale: How AI teams are simplifying and scaling popular AI/ML use cases using Ray	Learn how Ray customers simplify and scale any AI or Python workload using Ray, the fastest-growing unified compute framework for scaling machine learning workloads. Find out how organizations successfully develop and deploy large-scale machine learning applications and address demand forecasting, NLP, scalable reinforcement learning, recommendation systems, estimated on-time arrival, dynamic pricing, and more.
Telmai: Data Observability - The New Tool in any Modern Data Stack	Every company has become a data company. However, achieving data reliability in a complex data environment is becoming more and more challenging. We believe the rightly architected data observability approach can solve these problems early and often. Drawing from the experiences of our early adopters, we will discuss the pain points that today’s observability solutions can solve, and how best to roll out a solution. We will take a look at different approaches to Data Observability, the pros and cons of each approach, and the best use cases and fit for each.
Snorkel AI: Bridging the Gap Between Large Language Models and Enterprise AI	Karl Schutz, Enterprise Sales, will discuss how enterprises are adopting data-centric workflows and large language models like PaLM, GPT-3 and CLIP to accelerate AI development 10-100x. For example, a global retailer with a massive, rapidly expanding catalog relies on machine learning to classify and recommend products. Catalog maintenance is critical to the bottom line, but manually labeling data to train dozens of models was slow, painful, and expensive. With Snorkel AI and Google Cloud, the retailer was able to distill knowledge from foundation models to build smaller, deployable classification models in just 10 days (vs a projected 8 full-time equivalent years!) while improving model accuracy up to 22%.
Q&A and Closing Discussion

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