📅 2025-12-10 — Session: Developed data pipeline for AI benchmarking
🕒 17:40–18:20
🏷️ Labels: Mlperf, Data Pipeline, Ai Benchmarking, Data Ingestion, Data Platform
📂 Project: Dev
Session Goal
The session aimed to develop a comprehensive data pipeline to benchmark AI models and chips, focusing on MLPerf and other relevant performance metrics.
Key Activities
- Conducted search queries related to MLPerf inference results, Hugging Face benchmarks, NVIDIA H100 specifications, and Google TPU announcements.
- Outlined a detailed data pipeline plan for delivering a clean dataset comparing AI models and chips, including a data schema and backend implementation checklist.
- Developed a structured approach for scraping high-value sources related to MLPerf benchmark data.
- Created an actionable ingestion plan for extracting, parsing, and normalizing data from various high-value sources related to MLPerf results.
- Designed a data platform for the chips-vs-models project with options for both a Lightweight MVP and a Scalable Production path.
- Explored and mapped the Epoch.ai site for data extraction and analytics.
- Formulated a plan for implementing a relational model tailored for data lakes, including SQL and pandas snippets for data processing.
Achievements
- Successfully outlined the data pipeline and ingestion plans for AI performance benchmarking.
- Established a clear roadmap for data platform design and implementation.
Pending Tasks
- Further development and testing of the data pipeline and ingestion processes.
- Implementation of the relational model and derived datasets.
- Continued exploration of additional benchmarking sources and metrics.