Build vs. Buy Economics for ML Tools
Economic 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.
8 docs tagged with "cost-management"
View all tagsCloud FinOps for ML
Financial operations for ML cloud spend - FinOps maturity model, reserved instances, spot strategy, multi-account cost attribution, and ML budget forecasting.
Cost Attribution and Accountability
Making ML teams own their costs - tagging strategy, per-model cost dashboards, chargeback model design, cost anomaly detection, and engineering incentives for cost efficiency.
Inference Cost Optimization
Reducing ML serving costs at scale - quantization ROI, batching economics, instance right-sizing, caching strategies, and LLM cost-per-token analysis.
ML Infrastructure Cost Model
Understanding what drives ML costs - building a cost-per-request model for your ML system from scratch, and computing unit economics the CTO will believe.
Module 15 - Cost Management for ML
Financial operations for ML systems - understanding costs, optimizing training and inference, cloud FinOps, build vs. buy analysis, and cost attribution.
Token Counting and Context Management
Tiktoken, tokenisation internals, context window management, sliding window strategies, and building cost-aware LLM applications.
Training Cost Optimization
Reducing ML training costs systematically - spot instances, mixed precision, gradient checkpointing, compute-optimal training (Chinchilla), and distributed training overhead.