Description
Effective machine learning (ML) prototyping is critical for developing successful models and applications. Python, with its robust ecosystem of tools, plays a significant role in this process.
This talk will bring a fresh perspective on how we, at Meta, provide tools for efficient ML prototyping for our researchers and ML engineers. We will first go through a comprehensive overview of the ML lifecycle and then deep dive into the ML prototyping phase, emphasizing on its significance. We will focus on conda for environment management and jupyter notebooks for interactive development — two powerful tools that streamline development and experimentation at Meta and in the industry, in general. In the last part of the talk, I will highlight interesting examples of improvements made to these tools at Meta for efficient prototyping, accelerating the developer productivity in the AI space.