All Shorts
A collection of engineering thoughts, architecture decisions, and lessons learned.
Day 16: Explaining ML's Neglected Concepts - 𝗕𝗮𝘁𝗰𝗵 𝘃𝘀. 𝗦𝘁𝗿𝗲𝗮𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴
Most tutorials teach you batch. Most jobs eventually need stream. They're not just different speeds. They're different assumptions about when data shows up.
Read short →Day 15: Explaining ML's Neglected Concepts - 𝗢𝗟𝗔𝗣
The reason your "fast" database still can't answer a simple analytics question. You trained the model. The pipeline runs. Then someone asks a question and your stack chokes.
Read short →Day 14: Explaining ML's Neglected Concepts - 𝗢𝗟𝗧𝗣
Most ML engineers interact with OLTP systems every day and still can't explain what makes them different. It's the database layer built to power live applications.
Read short →Day 13: Explaining ML's Neglected Concepts - 𝗜𝘀𝘀𝘂𝗲𝘀 𝗼𝗳 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
Continual learning looks clean in research. Shipping it is a different conversation. Your data stream doesn't tell you when the distribution shifted - you find out when accuracy drops.
Read short →Scaling RAG Pipelines in Production
Lessons learned from serving 100+ concurrent vector search queries per second without melting the database.
Read short →The Unbearable Weight of Massive Time-Series Data
How we processed 1.2TB of daily IoT sensor data from 50,000 cars using PySpark and survived to tell the tale.
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