Description:

AI has rapidly transitioned from a science fiction concept to a ubiquitous reality, driving a highly competitive market with myriad potential applications. However, the development of AI-driven software faces significant challenges, particularly in terms of infrastructure and computing power requirements. Accessing the necessary hardware resources, especially graphics processing units (GPUs), can be a major obstacle, often necessitating cloud solutions and a broader range of expertise within engineering teams. This workshop introduces Covalent Cloud, a powerful platform that accelerates the development of high-compute applications by providing powerful abstractions and streamlining the process of building AI-driven software. Participants will learn how to build a Zero-Data Model Foundry, an application that automates the creation of custom machine learning models without requiring the end-user to provide or point to existing fine-tuning data. Instead, a large language model (LLM) will be utilized to generate the necessary data. Through a practical example, participants will construct an entire Software-as-a-Service (SaaS) platform on top of Covalent Cloud, leveraging only a local Python environment. The tutorial aims to demonstrate how Covalent Cloud simplifies the development of AI applications, reducing the need for extensive hardware resources and specialized expertise, thereby enabling more efficient and accessible innovation in the field of AI.

Prerequisites:

General knowledge of machine learning & in particular large language models LLMs, & what it means to fine-tune a model.

Python coding will be standard - some previous exposure to Python decorators is helpful.

Content URLs:

https://github.com/AgnostiqHQ/covalent.

https://docs.covalent.xyz/docs/os_main.

Speaker Info:

There will be two speakers:

Name: Santosh Radha Bio: Santosh is the Head of Product/Research at Agnostiq, where he plays a pivotal role in shaping the company's product strategy, particularly through the development of Covalent which is designed to significantly enhance the scalability and performance of next-generation AI applications and large-scale scientific simulations across multi-cloud environments. Santosh holds a Ph.D in theoretical physics from Case Western Reserve University. LinkedIn: https://www.linkedin.com/in/santoshkumarradha/ Github: https://github.com/santoshkumarradha

Name: Ara Ghukasyan Bio: Ara is a Research Software Engineer at Agnostiq Inc. He has a B.Sc. in Math & Physics and a Ph.D. in Engineering Physics from McMaster University in Hamilton, Ontario. Ara’s interests include Machine Learning, Physics, and Quantum Computing. In his spare time, he also enjoys playing guitar and bass. LinkedIn: https://www.linkedin.com/in/ara-ghukasyan-782029223/ Github: https://github.com/araghukas

Speaker Links:

Santosh: https://www.linkedin.com/in/santoshkumarradha/ https://github.com/santoshkumarradha

Ara: https://www.linkedin.com/in/ara-ghukasyan-782029223/ https://github.com/araghukas

Recent presentation at PyCon US https://us.pycon.org/2024/schedule/presentation/150/