Optimize, Evaluate, and Forecast Your Cloud Spend

Overview

Are you spending 30% more than necessary on cloud infrastructure? This Short Course was created to help ML and AI professionals accomplish strategic cloud cost optimization through data-driven analysis and predictive modeling. By completing this course, you'll be able to identify resource waste through systematic allocation versus utilization analysis, make informed decisions on cloud pricing strategies that reduce operational expenditure, and build sophisticated forecasting models that enable proactive budget planning and financial governance. By the end of this course, you will be able to: • Analyze resource allocation versus utilization to identify optimization opportunities • Evaluate cloud pricing strategies to reduce operational expenditure • Create a cost forecasting model using historical usage and planned project data This course is unique because it combines technical infrastructure analysis with financial modeling expertise, providing hands-on experience with enterprise-grade cost optimization tools and real-world scenarios that mirror actual cloud operations challenges. To be successful in this project, you should have a background in cloud infrastructure management and basic data analytics experience.

Syllabus

Module 1: Resource Allocation Analysis

Learners will master systematic analysis of cloud resource allocation patterns versus actual utilization to identify waste, performance bottlenecks, and cost optimization opportunities.

Module 2: Cloud Pricing Strategy Evaluation

Learners will master comprehensive evaluation of cloud pricing models to make strategic procurement decisions that optimize costs while maintaining performance requirements for AI and ML workloads.

Module 3: Cost Forecasting Model Creation

Learners will master the development of sophisticated cost forecasting models that integrate historical consumption patterns with planned business initiatives to enable proactive budget planning and strategic financial governance.