Introduction

Running machine learning (ML) workloads in the cloud can become prohibitively expensive when teams overlook resource orchestration. Large-scale data ingestion, GPU-based inference, and ephemeral tasks often rack up unexpected fees. This article offers a detailed look at advanced strategies for cost management, including:

Dynamic Extract, Transfer, Load (ETL) schedules using SQL triggers and partitioning
Time-series modeling—Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet—with hyperparameter tuning
GPU provisioning with NVIDIA DCGM and multi-instance GPU configurations
In-depth autoscaling examples for AI services

Our team reduced expenses by 48% while maintaining performance for large ML pipelines. This guide outlines our process in code.