[] Please like, share, and subscribe to our MLOps channels!
4
[] Anand's tech background
5
[] Fun at Optimization Level
6
[] Trying all APIs
7
[] Models evaluation decision tree
8
[] Weights and Biases Ad
9
[] AI Stack that understands the code
10
[] Tools for the Guard Rails
11
[] Seeking solutions before presenting to LLM
12
[] Prompt-Driven Development Insights
13
[] Prompting best practices
14
[] Unneeded complexities
15
[] Cost-benefit analysis of buying GPUs
16
[] ML Build vs Buy
17
[] Best practices for debugging code assistant
18
[] Wrap up
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Grab it
Explore the impact of Large Language Models (LLMs) on tech stacks and product development in this insightful MLOps podcast episode featuring Anand Das, Co-founder and CTO of Bito. Discover how Anand's team developed the popular "explain code" Chrome extension and expanded it to other platforms. Learn about Anand's extensive background in tech, including his roles at Eyeota, PubMatic, and various engineering positions. Gain valuable insights into model evaluation, AI stacks for code understanding, prompt-driven development, and best practices for prompting and debugging code assistants. Delve into discussions on the cost-benefit analysis of GPU investments and the build vs. buy decision in machine learning. This 56-minute conversation covers a wide range of topics, from Anand's preferred coffee to the complexities of modern tech development, offering a comprehensive look at the evolving landscape of AI-driven software engineering.
Impact of LLMs on the Tech Stack and Product Development - MLOps Podcast #188