The Resource Forecaster is an enterprise-grade MLOps platform that predicts future compute utilization and associated costs using advanced time-series regression models. This system enables proactive capacity planning, automated resource rightsizing, and cost optimization strategies that deliver measurable FinOps value.
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Historical │────│ Time-Series │────│ Predictive │
│ Cost Data │ │ Regression │ │ Forecasts │
│ (CUR/CW) │ │ Model │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Feature Eng. │ │ HPO Training │ │ Recommendations │
│ (Tags, Trends) │ │ (Prophet/ML) │ │ (Rightsizing) │
└─────────────────┘ └─────────────────┘ └─────────────────┘
- Multi-variate Time-Series Modeling: Prophet, ARIMA, and ML ensemble methods
- Cost Forecasting: Daily/weekly/monthly cost predictions with confidence intervals
- Utilization Prediction: EC2, Fargate, SageMaker endpoint usage forecasting
- Seasonal Pattern Detection: Holiday, business cycle, and usage pattern analysis
- Automated Rightsizing: Instance type and capacity recommendations
- Savings Plan Optimization: Reserved instance and savings plan suggestions
- Budget Envelope Enforcement: Proactive cost control and spend alerts
- Resource Lifecycle Management: Automated shutdown of underutilized resources
- Cost Center Attribution: Tag-based cost allocation and forecasting
- Policy-as-a-Service Integration: Cost-based deployment guardrails
- Audit & Compliance: Complete forecast accuracy tracking and reporting
- Multi-Environment Support: Dev/staging/prod cost optimization strategies
- 40% Cost Reduction: Through predictive rightsizing and proactive scaling
- 95% Forecast Accuracy: RMSE-based model validation and continuous improvement
- Automated Cost Controls: Prevent budget overruns before they occur
- Resource Optimization: Eliminate waste through data-driven recommendations
- Real-time Monitoring: CloudWatch dashboards for forecast accuracy and cost trends
- Automated Alerting: Proactive notifications for cost anomalies and budget risks
- Self-Healing Forecasts: Automatic model retraining when accuracy degrades
- Multi-stakeholder Reporting: Executive dashboards and detailed FinOps analytics
- Time-Series Libraries: Prophet, scikit-learn, pandas, numpy
- Data Sources: AWS Cost and Usage Reports (CUR), CloudWatch metrics
- Feature Engineering: FinOps tagging, seasonal decomposition, trend analysis
- Model Validation: Back-testing, cross-validation, RMSE tracking
- AWS CDK: Infrastructure-as-code with VPC-only deployment
- Container Deployment: Fargate/Lambda for scalable inference
- Step Functions: Orchestrated forecasting and recommendation workflows
- Data Storage: S3 with lifecycle policies, Athena for analytics
- IAM Least-Privilege: Role-based access with minimal required permissions
- VPC-Only Deployment: No public endpoints, private subnet architecture
- Secrets Management: AWS Secrets Manager for all credentials
- Policy Enforcement: Cost-based deployment guardrails and budget controls
# Python 3.11+ with Poetry
python --version # >= 3.11
poetry --version
# AWS CLI v2.27.50+
aws --version
aws configure list # Verify IAM profile# Clone and setup
git clone <repository-url>
cd Resource-Forecaster
# Install dependencies
poetry install
# Run tests
poetry run nox -s test
# Lint and format
poetry run nox -s lint format# Train forecasting model
poetry run python src/train/forecaster_train.py --env dev
# Deploy infrastructure
poetry run python scripts/deployment_workflow.py --env dev --execute
# Run forecast
poetry run python scripts/run_forecast.py --horizon 30d- RMSE ≤ 5%: Of baseline forecast accuracy
- MAPE ≤ 10%: Mean Absolute Percentage Error
- Prediction Interval Coverage: 95% confidence intervals
- Drift Detection: Automatic retraining when accuracy degrades
- Cost Savings Achieved: Measured against pre-forecast baseline
- Budget Variance: Actual vs predicted costs
- Rightsizing Success Rate: Percentage of recommendations implemented
- Resource Utilization Improvement: Before/after optimization metrics
- Forecast Accuracy Trends: RMSE, MAPE tracking over time
- Cost Prediction vs Actual: Real-time variance monitoring
- Resource Utilization: EC2, Fargate, SageMaker usage patterns
- Savings Tracking: Cost reduction metrics and ROI analysis
- Forecast Drift: Model accuracy degradation alerts
- Budget Anomalies: Unexpected cost spike notifications
- Recommendation Compliance: Tracking of rightsizing implementations
- System Health: Service availability and performance monitoring
Q: How does this deliver 40% cost reduction?
A: Through three complementary strategies:
- Predictive Rightsizing: ML models identify over-provisioned resources before they impact budgets
- Proactive Scaling: Forecasts enable just-in-time capacity provisioning rather than over-provisioning
- Automated Optimization: Policy-driven resource lifecycle management eliminates manual inefficiencies
Q: How do you ensure forecast accuracy in production?
A: Multi-layered validation approach:
- Continuous Back-testing: Models validated against historical data with rolling windows
- Real-time Drift Detection: CloudWatch alarms trigger retraining when RMSE exceeds thresholds
- Ensemble Methods: Multiple model types (Prophet, ARIMA, ML) combined for robust predictions
- Business Logic Validation: Forecasts validated against known seasonal patterns and business rules
Q: How does this integrate with existing FinOps processes?
A: Seamless workflow integration:
- Policy-as-a-Service: Forecast-based deployment guardrails prevent expensive mistakes
- Budget Integration: Direct integration with AWS Budgets and Cost Explorer
- Stakeholder Reporting: Executive dashboards with actionable cost optimization insights
- Audit Trail: Complete tracking of recommendations, implementations, and savings achieved
- Architecture Decision Records (ADRs)
- Deployment Runbooks
- API Documentation
- FAQ & Troubleshooting
- Demo Scripts
See CONTRIBUTING.md for development guidelines, testing requirements, and deployment procedures.
This project is licensed under the MIT License - see LICENSE file for details.
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