WriteUp.AI is my newest project. There's a lot of technical components that happened behind the scenes, so it deserves a deep dive into a lot of the technical architecture and mistakes I made along the way.
Note: This article link is a temporary placeholder since I normally spend 40+ hours on each of these writeups. Some of my previous articles that been well-received are
In this blog article, I'm going to detail everything I learned while building WriteUp.AI a tool that uses cutting edge ML AI articles to help people write. I estimated it would take me about three weeks to build it, it ended up taking two months as I kept on adding one more feature. During this, I learned a lot about deploying ML. It should be finished this in the next week or so, but I wanted to list out all the topics I outlined to write.
- A Quick Recap of Tech Architecture
- Three Weeks in Peru
- The 90/90 Rule
- Overengineered or Overutilized?
- Inference And Other Scary ML Slang Terms Explained
- K80s, V100s or P100s? (And the envy of TPUs)
- Thompson’s Rule of Telescopes (How ML-Ops is never straight forward …)
- Container This … Container That … Wait, where’s my video card?
- What do you mean out of memory?!
- Bad States with Large Tensors Leads To Other Odd Problems
- I've Entered Cloud Prison
- Things I Tried That Didn't Quite Work Out (Hint: Everything)
- I had to rewrite the backend and microservice architecture twice ...
- I Thought I’d Learn More About Neural Networks …
- Testing Out Autoscaling with Async
- Shortening Feedback Cycles with Automation
- Finding Bottlenecks: CPU, GPU, or Memory?
- Google Cloud Regions. Pick one or pick all?
- Battle of the Feature Creep
- Making Difficult Decisions Between TensorFlow Serve, Kubernetes, TorchScript and Other Model Deployment Questions
- TorchScript Performance Speedup (And Other Optimizations)
- Measuring Inference Times with Cascade Lake Vs V100
- Measure Twice, Cut Once? No. You Should Get Faster at Cutting.
- Special Thanks To
- Lot of my friends helped me test this and improve the UX.
- Why PyTorch? Why TensorFlow?
- Dealing with Burnout
I'll alert the subscribers on the mailing list when I'm done, or just come back to this article in a week or-so. Thanks!