Karsten S. Nielsen PRO

karstenskyt's activity

• All
• Models
• Datasets
• Spaces
• Buckets
• Papers
• Collections
• Community
• Posts
• Upvotes
• Likes
• Articles

posted an update about 1 month ago

view post

Post

2285

🚀 𝗟𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗜/𝗠𝗟 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱

Now that our Taipy architecture is humming along on Hugging Face Spaces, we just shipped the most complex feature of the (𝘙𝘪𝘨𝘩𝘵! 𝘓𝘶𝘹𝘶𝘳𝘺!) 𝘓𝘢𝘬𝘦𝘩𝘰𝘶𝘴𝘦 to date: the 𝗔𝗜/𝗠𝗟 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱.

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:

  • 𝗔𝗻 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗗𝗔𝗚: Powered by Cytoscape.js, it visually maps exactly how our models and data grids feed into each other.

  • 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: Tracks run volumes and data freshness SLAs across the entire platform.

  • 𝗔 𝟯-𝘁𝗶𝗲𝗿 𝗵𝘆𝗯𝗿𝗶𝗱 𝗰𝗼𝘀𝘁 𝗲𝗻𝗴𝗶𝗻𝗲: Merges "cold" Databricks billing data with "warm/hot" live HF Jobs estimates to give a unified view of pipeline expenses.

Check out the live interactive graph here:
luxury-lakehouse/soccer-analytics-app

See translation

🔥

5

5

+

Reply

posted an update about 1 month ago

view post

Post

154

🚀 𝗟𝘂𝘅𝘂𝗿𝘆 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 𝗨𝗜 𝗨𝗽𝗴𝗿𝗮𝗱𝗲: 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝘁𝗼 𝗧𝗮𝗶𝗽𝘆

I’m excited to share a major frontend architecture upgrade for the (𝘙𝘪𝘨𝘩𝘵! 𝘓𝘶𝘹𝘶𝘳𝘺!) 𝘓𝘢𝘬𝘦𝘩𝘰𝘶𝘴𝘦 open-source soccer analytics platform. We have officially migrated the dashboard UI from 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 to 𝗧𝗮𝗶𝗽𝘆, and it is now live on Hugging Face Spaces.

𝗪𝗵𝘆 𝘁𝗵𝗲 𝘀𝘄𝗶𝘁𝗰𝗵?

While Streamlit was fantastic for prototyping our initial 12 dashboards, we started running into some persistent "jittery" rendering issues as the app grew more complex—specifically when handling 𝟯𝟴𝗠+ 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘀 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 𝗮𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀 𝗯𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝘁𝗮𝘀𝗸𝘀 and 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴—capabilities we will be heavily leaning into as we start rolling out some advanced, long-running ML training pipelines soon. 👀

Check out the smoother, faster V2 of the platform here:
luxury-lakehouse/soccer-analytics-app

See translation

👍

1

1

+

Reply

posted an update about 1 month ago

view post

Post

188

🚀 𝗪𝗲 𝗷𝘂𝘀𝘁 𝗼𝗽𝗲𝗻𝗲𝗱 𝘁𝗵𝗲 𝗱𝗼𝗼𝗿𝘀 𝘁𝗼 𝗹𝘂𝘅𝘂𝗿𝘆-𝗹𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲! Our full 12-page open-source soccer analytics platform is now live and publicly accessible on Hugging Face Spaces.

We successfully migrated our entire backend Streamlit dashboard to a Docker Space. Instead of being locked behind workspace accounts, anyone can now directly explore our models, 38M+ tracking frames, and advanced metrics (xT, PAUSA, DEFCON) in the browser.

To prepare for the public release, we ran a massive optimization sprint (what used to be days of normal engineering, now condensed into a few hours of "Claude Time"):

⚡ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Eliminated O(n²) bottlenecks, dropping ingestion pipeline runs from 36 minutes to <30 seconds.

🧠 𝗨𝗫: Applied a deep Cognitive Interface Audit across all surfaces to ensure the data is intuitive and human-readable.

The Hugging Face ecosystem now natively handles our entire public-facing presentation layer—Models, Datasets, Gradio Demos, and the full Streamlit App.

𝗧𝗿𝘆 𝘁𝗵𝗲 𝗹𝗶𝘃𝗲 𝗮𝗽𝗽: luxury-lakehouse/soccer-analytics-app

𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲:

luxury-lakehouse

See translation

👀

1

1

+

Reply

posted an update about 1 month ago

view post

Post

141

⚽ We've been building 𝚕𝚞𝚡𝚞𝚛𝚢-𝚕𝚊𝚔𝚎𝚑𝚘𝚞𝚜𝚎—an open-source soccer analytics platform—and we're using the Hugging Face Hub as our public distribution layer.

"Luxury! We used to dream of serverless!" We replaced a traditional 6-service AWS pipeline with a unified lakehouse architecture. While Databricks handles our backend Medallion architecture (processing 𝟯𝟴𝗠+ 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘀 from 5 vendors), we rely entirely on the HF ecosystem for public access and specific compute workloads.

I just updated our org page with a full architecture breakdown showing how we integrate 𝗛𝗙 𝗠𝗼𝗱𝗲𝗹𝘀, 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀, 𝗚𝗿𝗮𝗱𝗶𝗼 𝗦𝗽𝗮𝗰𝗲𝘀, and use 𝗛𝗙 𝗝𝗼𝗯𝘀 for serverless Expected Threat (xT) computation with a bidirectional Databricks sync.

Check out the full architecture details here: 👇

luxury-lakehouse

See translation

🚀

1

1

+

Reply