Platform to serve ML/DL models at low latency

Hacker News

I've always had a hard time deploying ML models into production. The traditional approach is to use Flask, Gunicorn with Nginx. This requires a lot of setup time. Also, inferring model with flask is slow and requires custom code for caching and batching. Scaling in multiple machines is also hard. We have created panini.ai as a solution. https://www.panini.ai/ is a platform to serve ML/DL models at low latency and makes it super easy to deploy AI models in the cloud. Once, deployed in the cloud it will provide you with an API key to infer the model. Our backend is written in C++, which provides very low latency during model inference and the model is stored in Kubernetes so, it is scalable to multiple nodes. We take care of caching and batching inputs during model inference. I have also created a YouTube tutorial on how to use panini: https://www.youtube.com/watch?v=tCz-fi_NheE&t= Please let me know what you guys think. If you have any questions, please email me at avin@panini.ai

Visit product View on Hacker NewsFeb 24, 2019

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: nginx, ide, io · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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