01Module 15 - Cost Management for MLFinancial operations for ML systems - understanding costs, optimizing training and inference, cloud FinOps, build vs. buy analysis, and cost attribution.02ML Infrastructure Cost ModelUnderstanding what drives ML costs - building a cost-per-request model for your ML system from scratch, and computing unit economics the CTO will believe.03Training Cost OptimizationReducing ML training costs systematically - spot instances, mixed precision, gradient checkpointing, compute-optimal training (Chinchilla), and distributed training overhead.04Inference Cost OptimizationReducing ML serving costs at scale - quantization ROI, batching economics, instance right-sizing, caching strategies, and LLM cost-per-token analysis.05Cloud FinOps for MLFinancial operations for ML cloud spend - FinOps maturity model, reserved instances, spot strategy, multi-account cost attribution, and ML budget forecasting.06Build vs. Buy Economics for ML ToolsEconomic analysis for ML tooling decisions - TCO framework, self-hosted vs. managed analysis, hidden costs of self-hosting, and a full financial case for W&B vs. MLflow.07Cost Attribution and AccountabilityMaking ML teams own their costs - tagging strategy, per-model cost dashboards, chargeback model design, cost anomaly detection, and engineering incentives for cost efficiency.
