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

Thu, Apr 13, 4:00 PM (UTC)

38

RSVPs

AI and Machine Learning

About this event

Companies use AI and machine learning to drive innovation and create a competitive advantage in their respective marketplaces. In this virtual event, four fast-growing startups share their experiences using AI and machine learning.
Excitement about AI is everywhere right now, so we wanted to bring together some of the top startups that are disrupting the AI space and bringing valuable solutions to enterprise customers. 
The promise of AI for business is immense. AI can help businesses automate tasks, improve efficiency, and make better decisions. However, AI is still a relatively new technology. This event will cover topics such as:
Increasing affordability and accessibility of AI, which is driving AI adoption in the enterprise space
Use of AI to automate a wide range of tasks, from customer service to fraud detection
Use of AI to improve efficiency and decision-making in a variety of industries
At the end of this webinar, participants will have a better understanding of AI and its potential applications. We will have discussed best practices, success stories, and practical lessons about what to consider if this technology is something you are considering implementing as part of your business's strategic roadmap for the future.
Visit our previous content about AI/ML and join the C2C Community to continue the conversation.

Speakers

Bonnie Donovan

Google Cloud

Head of Ecosystem Growth & Partnership Management

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, 13 April
9:00 AM - 10:00 AM (PDT)

Agenda

4:00 PM	Welcome and Intro	Bonnie Donovan, Head of Ecosystem Growth & Partnership Management, Google Cloud
4:10 PM	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.
4:20 PM	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.
4:30 PM	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.
4:40 PM	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%.
4:50 PM	Q&A and Closing Discussion