Todd Dube

💡 MLOps best practices I've learned deploying models at scale: 1) Version everything - data, models, and configs. 2) Implement robust monitoring from day one. 3) Automate retraining pipelines. 4) Build in human-in-the-loop checkpoints. The gap between a working notebook and production ML is where most projects fail.

💾➡️🤖 From 6502 Assembly on Commodore 64 to designing AI systems - my tech journey spans 4 decades. The fundamentals remain the same: understand the problem deeply, write clean code, and always keep learning. Today's AI engineers could benefit from understanding systems at the lowest level - it gives invaluable perspective on optimization.

⚡ Pro tip for AI developers: Your model's context window is precious real estate. Optimize prompts ruthlessly, use RAG strategically, and always measure token usage. I've seen 40% cost reductions just from smarter context management. Efficiency isn't just about performance - it's about sustainability.