WEBINAR "Making Machine Learning Easy for Engineers with Declarative ML"

To access this webinar, please register here: https://hubs.li/Q01DptBx0

Topic: "Making Machine Learning Easy for Engineers with Declarative ML"

Speaker#1: Devvret Rish, Co-Founder and Chief Product Officer, Predibase
Prior to Predibase, he was a ML PM at Google working across products like Firebase, Google Research and the Google Assistant as well as Vertex AI. While there, Dev was also the first product manager for Kaggle – a data science and machine learning community with over 8 million users worldwide. Dev’s academic background is in computer science and statistics, and he holds a masters in computer science from Harvard University focused on machine learning.

Speaker#2: Geoffrey Angus, Machine Learning Engineer at Predibase
Prior to Predibase, he worked at Google Research on the Perception team. While there, he implemented, trained, and deployed large multi-modal models for Image Search and Google Lens. Geoffrey holds a Bachelor's and Master's in Computer Science from Stanford University, where he conducted machine learning research on weak supervision and computer vision for medical imaging applications.

Abstract:
Making ML easy and accessible for engineers through low-code interfaces.

At some point, every engineering team’s roadmap has included an item to “improve their product with machine learning”. Depending on the product, this can mean anything from adding personalization, recommender systems, fraud detection, or any ML-powered feature that leverages collected data to improve the user experience.
The challenge: most organizations lack sufficient data science resources to rapidly build custom models in-house, leaving engineering teams roadblocked on their ML projects and the OKR is pushed back another quarter.

A new generation of declarative machine learning tools—built on foundations pioneered at Uber, Apple, and Meta—aims to change this dynamic by making machine learning accessible to engineers and teams that are ML-curious. Declarative ML systems simplify model building with a config-driven approach rooted in engineering best practices like automation and reusability, in a similar way that Kubernetes revolutionized managing infrastructure. With these capabilities, developers can build powerful production-grade ML systems for practical applications in minutes.

Join this webinar and demo to learn:
- About declarative ML systems, incl. open-source Ludwig from Uber
- How to build state-of-the-art machine learning and deep learning models in less than 15 lines of code
- How to rapidly train, iterate, and deploy a multimodal deep learning model with Ludwig and Predibase

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