Now that our Taipy architecture is humming along on Hugging Face Spaces, we just shipped the most complex feature of the (๐๐ช๐จ๐ฉ๐ต! ๐๐ถ๐น๐ถ๐ณ๐บ!) ๐๐ข๐ฌ๐ฆ๐ฉ๐ฐ๐ถ๐ด๐ฆ to date: the ๐๐/๐ ๐ ๐ช๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐๐ ๐๐ฎ๐๐ต๐ฏ๐ผ๐ฎ๐ฟ๐ฑ.
Managing 16 different machine learning pipelines (from Expected Goals to Space Creation) across Databricks Serverless and HF Jobs is a logistical challenge. To solve this, we built a dynamic operations center (the 13th page in our app).
It features:
ย ย โข ๐๐ป ๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐๐ฒ ๐ฑ๐ฒ๐ฝ๐ฒ๐ป๐ฑ๐ฒ๐ป๐ฐ๐ ๐๐๐: Powered by Cytoscape.js, it visually maps exactly how our models and data grids feed into each other.
ย ย โข ๐ฅ๐ฒ๐ฎ๐น-๐๐ถ๐บ๐ฒ ๐บ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด: Tracks run volumes and data freshness SLAs across the entire platform.
ย ย โข ๐ ๐ฏ-๐๐ถ๐ฒ๐ฟ ๐ต๐๐ฏ๐ฟ๐ถ๐ฑ ๐ฐ๐ผ๐๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ: Merges "cold" Databricks billing data with "warm/hot" live HF Jobs estimates to give a unified view of pipeline expenses.
Iโm excited to share a major frontend architecture upgrade for the (๐๐ช๐จ๐ฉ๐ต! ๐๐ถ๐น๐ถ๐ณ๐บ!) ๐๐ข๐ฌ๐ฆ๐ฉ๐ฐ๐ถ๐ด๐ฆ open-source soccer analytics platform. We have officially migrated the dashboard UI from ๐ฆ๐๐ฟ๐ฒ๐ฎ๐บ๐น๐ถ๐ to ๐ง๐ฎ๐ถ๐ฝ๐, and it is now live on Hugging Face Spaces.
While Streamlit was fantastic for prototyping our initial 12 dashboards, we started running into some persistent "jittery" rendering issues as the app grew more complexโspecifically when handling ๐ฏ๐ด๐ + ๐๐ฟ๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด ๐ณ๐ฟ๐ฎ๐บ๐ฒ๐ across 5 professional data providers.
Rebuilding the app in Taipy (running via the Docker SDK on HF Spaces) immediately smoothed out those state-management hiccups. The difference is exceptionally noticeable when interacting with our native Plotly charts, like the Pass Networks and Pitch Control surfaces.
More importantly, this architectural switch sets the foundation for our next major roadmap milestone. Taipy natively excels at managing ๐ฎ๐๐๐ป๐ฐ๐ต๐ฟ๐ผ๐ป๐ผ๐๐ ๐ฏ๐ฎ๐ฐ๐ธ๐ด๐ฟ๐ผ๐๐ป๐ฑ ๐๐ฎ๐๐ธ๐ and ๐ฎ๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐ ๐๐ฟ๐ฎ๐ฐ๐ธ๐ถ๐ป๐ดโcapabilities we will be heavily leaning into as we start rolling out some advanced, long-running ML training pipelines soon. ๐