Building ML solutions from scratch is challenging because of a variety of reasons: the long development cycles of writing low level machine learning code and the fast pace of state-of-the-art ML methods to name a few. On the other hand, solutions that automate the ML model development process are often opaque and hard to iterate on, resulting in users churning out. In this talk I’ll cover declarative ML systems, and how they address key issues that help shorten the time taken to bring ML models to production.
Speaker
Shreya Rajpal
Founder @Guardrails AI, Experienced ML Practitioner with a Decade of Experience in ML Research, Applications and Infrastructure
Shreya Rajpal is the creator and maintainer of Guardrails AI, an open source platform developed to ensure increased safety, reliability, and robustness of large language models in real-world applications. Her expertise spans a decade in the field of machine learning and AI. Most recently, she was the founding engineer at Predibase, where she led the ML infrastructure team. In earlier roles, she was part of the cross-functional ML team within Apple's Special Projects Group and developed computer vision models for autonomous driving perception systems at Drive.ai.
Slides are not available
