01Module 9 - Cost & FinOps for AIMaster AI infrastructure economics - from cost modeling to FinOps culture - so you can build powerful systems without burning your budget.02AI Cost DriversCompute, storage, data transfer, and API costs in AI systems.03ML Cost ModelsLearn to build a complete ML cost model - from compute and storage to hidden data transfer costs - so your team never gets blindsided by a $300K quarterly cloud bill.04LLM API Cost OptimisationCaching, prompt compression, model routing, and batching to reduce LLM costs.05Training Cost OptimizationReduce model training costs by 60–80% through spot instances, gradient checkpointing, mixed precision, and compute-optimal training - without sacrificing accuracy.06Inference Cost OptimizationReduce LLM inference costs by 60–80% through quantization, intelligent batching, right-sizing, and autoscaling - turning an $80K/month bill into $20K.07Training Cost EstimationFlop counting, GPU-hours, and estimating training costs before you run.08Build vs Buy AnalysisA rigorous financial and risk framework for deciding when to build ML infrastructure in-house vs use managed services - applied to feature stores, vector DBs, LLMs, and more.09Inference Cost OptimisationQuantisation, batching, speculative decoding, and right-sizing serving infrastructure.10Cloud Cost ManagementImplement full FinOps practice for ML teams - from commitment-based discounts and tagging strategies to budget alerts and spot instance automation.11Cloud Cost VisibilityTagging, cost allocation, unit economics, and cloud cost dashboards.12Model Efficiency EconomicsAnalyze the accuracy-cost Pareto frontier to determine when model improvements are economically justified - and how to build the business case for the current model being cost-optimal.13Spot and Preemptible InstancesUsing spot instances for training, checkpointing strategies, and interruption handling.14FinOps Culture for AI TeamsBudgets, showback/chargeback, and building cost-awareness into the ML workflow.15ML ROI and Business CasesBuild iron-clad ROI cases for ML investments - from quantifying recommendation system value to attributing A/B test results to long-term business outcomes.
