
Over four months, Josh Koch developed and enhanced capacity planning tools within the Netflix-Skunkworks/service-capacity-modeling repository. He built automated AWS instance family configuration generators and integrated EC2 pricing data, using Python and Jupyter Notebook to streamline data loading, deterministic output, and performance modeling. His work included implementing headroom estimation tooling based on Erlang-C models, expanding support for new AWS instance types, and refining data formatting for automation reliability. By focusing on API integration, infrastructure as code, and robust testing, Josh improved the accuracy and scalability of capacity modeling, reducing manual effort and enabling more data-driven, reproducible infrastructure decisions.

October 2025 monthly summary for the Netflix-Skunkworks service-capacity-modeling repo focused on expanding real-world coverage for capacity planning and ensuring reliable outputs in automation. The work delivers broader instance-type support, performance modeling improvements, and a reliability-focused bug fix with clear business value.
October 2025 monthly summary for the Netflix-Skunkworks service-capacity-modeling repo focused on expanding real-world coverage for capacity planning and ensuring reliable outputs in automation. The work delivers broader instance-type support, performance modeling improvements, and a reliability-focused bug fix with clear business value.
May 2025 monthly summary for Netflix-Skunkworks/service-capacity-modeling focusing on business value and technical achievements. Delivered automated AWS Instance Family Configuration Generator to improve capacity modeling. The tool automatically generates missing AWS instance family configurations, updates tests and instance family definitions to cover new instance types and performance characteristics, and broadens coverage of AWS hardware options. This automation reduces manual configuration, accelerates onboarding of new instance types, and increases accuracy of capacity planning across services.
May 2025 monthly summary for Netflix-Skunkworks/service-capacity-modeling focusing on business value and technical achievements. Delivered automated AWS Instance Family Configuration Generator to improve capacity modeling. The tool automatically generates missing AWS instance family configurations, updates tests and instance family definitions to cover new instance types and performance characteristics, and broadens coverage of AWS hardware options. This automation reduces manual configuration, accelerates onboarding of new instance types, and increases accuracy of capacity planning across services.
February 2025 Monthly Summary for Netflix-Skunkworks/service-capacity-modeling: Focused on delivering headroom estimation tooling to enable data-driven capacity planning and cost optimization.
February 2025 Monthly Summary for Netflix-Skunkworks/service-capacity-modeling: Focused on delivering headroom estimation tooling to enable data-driven capacity planning and cost optimization.
January 2025 monthly summary for Netflix-Skunkworks/service-capacity-modeling focused on delivering deterministic EC2 pricing integration and robust data handling to sharpen capacity planning and cost modeling. Key accomplishments include the delivery of the EC2 Pricing Data Integration and Deterministic Pricing Outputs feature, which fetches latest EC2 pricing from AWS, supports multiple pricing files sorted lexicographically, refactors pricing data loading and fetching for clarity and maintainability, ensures deterministic JSON output, and implements robust pricing merge logic with tests. The work is backed by targeted commits that show end-to-end development and quality improvements.
January 2025 monthly summary for Netflix-Skunkworks/service-capacity-modeling focused on delivering deterministic EC2 pricing integration and robust data handling to sharpen capacity planning and cost modeling. Key accomplishments include the delivery of the EC2 Pricing Data Integration and Deterministic Pricing Outputs feature, which fetches latest EC2 pricing from AWS, supports multiple pricing files sorted lexicographically, refactors pricing data loading and fetching for clarity and maintainability, ensures deterministic JSON output, and implements robust pricing merge logic with tests. The work is backed by targeted commits that show end-to-end development and quality improvements.
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