5 Tips to Reduce your ML Cloud Costs

Cloud platforms like AWS, GCP, and Azure provide robust managed services essential for machine learning (ML) workloads but can lead to high costs if not properly managed. Effective cost management begins with tracking and measuring expenses, enabling identification of costly models, teams, or datasets. Key strategies to control costs include implementing compute and data caching, utilizing checkpoints to recover from failures without restarting from scratch, and colocating data and compute resources to minimize expensive data transfers. GPU utilization can be maximized by optimizing data loading and memory management, employing GPU-optimized libraries, and using asynchronous operations. Infrastructure-level optimizations involve selecting the right cloud provider and pricing model, using spot instances or preemptible VMs, and leveraging auto-scaling for dynamic resource allocation. Beyond cloud costs, optimizing human resource expenses through strategic project prioritization, tool selection, and knowledge sharing can significantly impact overall ML cost efficiency.