Baseten – Build ML-powered applications

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Hi HN, Three months ago, I took a job at Baseten to help craft and document an application builder that lets data scientists build full-stack, production-ready applications around their ML models without worrying about containers, Flask, or React. From my first day, everyone was focused on what would happen today: opening up our public beta. I’m super excited to see what you build with Baseten. If you want to take Baseten for a full-speed test drive, follow along with this tutorial, where you can build and deploy an application in 20 minutes: https://docs.baseten.co/getting-started While Baseten is built for data scientists and machine learning engineers, something I’m particularly excited about that doesn’t come up often when we talk about Baseten is how it also makes building with ML available to people like me with a general software engineering background but no real experience with ML. With our library of pre-trained models, you can build and deploy an application around models for tasks like sentiment analysis, image classification, and speech transcription. By building applications around pre-trained models, I’ve gained a deeper understanding of the use cases, capabilities, and limitations of machine learning. If you want to play around with some models and applications without signing up for an account yet, check out our gallery ( https://baseten.co/gallery ) and try the demo apps. P.S. We are also hiring; I found Baseten from HN.

Visit product View on Hacker NewsApr 26, 2022

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

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

81%81% 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: started · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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.

Correct prediction on native model

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