8-5: ML Pipeline & MLOps
Modeling the vast difference between Model Training costs and Production Serving (Inference) costs.
🎯 What You'll Learn
- ✓ Separate Model Training vs Inference
- ✓ Calculate GPU burst consumption
- ✓ Implement MLOps experiment tracking
Training vs Inference Economics
In Machine Learning, building a model (Training) is a high-cost, burst-compute capital expense. Running that model for live users (Inference) is a low-cost, continuous operational expense. Treating them identically bankrupts budgets.
Using expensive A100 GPUs for live inference of a small, localized model is a catastrophic misallocation. Inference should be pushed to cheap CPUs or heavily quantized edge hardware wherever latency SLAs permit.
The goal of MLOps is to compress the time-to-market of a model while driving the per-request Inference cost to absolute zero.
The burst cost of running GPUs for 72 hours to compile the model weights.
The per-second cost of keeping the model hosted answering live traffic.
Implement auto-scaling-to-zero for your inference endpoints.
Action Items
Why is running continuous Machine Learning Inference on premium Training GPUs (like A100s) an economic mistake?
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Module Syllabus
Lesson 1: Training vs Inference Economics
In Machine Learning, building a model (Training) is a high-cost, burst-compute capital expense. Running that model for live users (Inference) is a low-cost, continuous operational expense. Treating them identically bankrupts budgets.Using expensive A100 GPUs for live inference of a small, localized model is a catastrophic misallocation. Inference should be pushed to cheap CPUs or heavily quantized edge hardware wherever latency SLAs permit.The goal of MLOps is to compress the time-to-market of a model while driving the per-request Inference cost to absolute zero.
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