Orchestrating the ML Lifecycle: An Engineering Blueprint

Constructing Scalable ML Architectures: A Step-by-Step overview

2023 hit, and there I was, as an architect traditionally dealing with predictable systems, yet I found myself intrigued by the ever-evolving beast that is Machine Learning (ML). Welcoming OpenAI ChatGPT, I started to deepen my knowledge by reading books like "Dive into Deep Learning", using IBM resources about Deploying and managing models and delving into AWS and NVIDIA's documentation, it struck me – ML is a whole different game.

The more I read about advanced deep learning architectures, the more I realized my architecture background wasn’t just ready for this “game”. These resources weren't just informative; they were a wake-up call. ML isn't just about algorithms and data; it's about the interplay of components, challenges, and the tough calls you need to make as an architect.

So here's my angle: How does an ML architecture actually work? What are its moving parts? And most importantly, what are the trade-offs and decisions that keep an ML architect awake at night? This blog post …