From Arduinos to LLMs: Exploring the Spectrum of ML - MLOps Podcast Episode 162

Dive into a comprehensive exploration of the MLOps spectrum, from large language models (LLMs) to TinyML, in this insightful podcast episode featuring Soham Chatterjee. Gain valuable insights into the challenges of scaling machine learning models, the limitations of relying solely on open AI's API, and strategies for deploying models in constrained environments. Learn about the integration of IoT with deep learning, the effective deployment of models in remote areas with limited power, and the utilization of small devices like Arduino Nano. Discover Soham's expertise in automated accounting, back-office management, and the intersection of machine learning and electronics. Explore topics such as edge computing, quantum computing, prompt engineering, and the realities of working with LLMs. Benefit from Soham's experience in building tools for automated accounting and back-office management, and gain insights into his courses on MLOps and TinyMLOps.

[] Soham's preferred coffee
[] Takeaways
[] Please share this episode with
[] Soham's background
[] From electrical engineering to Machine Learning
[] Deep learning, Edge Computing, and Quantum Computing
[] Tiny ML
[] Favorite area in Tiny ML chain
[] Applications explored
[] Operational challenges transformation
[] Building with Large Language Models
[] Most Optimal Model
[] LLMs path
[] Prompt engineering
[] Migrating infrastructures to new product
[] Your success where others failed
[] API Accessibility
[] Reality about LLMs
[] Compression angle adds to the bias
[] Wrap up