Cardamom – Deploy ML to AWS Lambda with a Function Call

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Hi HN! I wanted to share my machine learning compiler, hosting ML models on AWS Lambda. If you go to the linked page, there are instructions to run a script which generates a model using sklearn, feeds it to my endpoint, and then calls the created endpoint on lambda. In case you're unable to run a script, I've also included a video on the page. Unlike other ML hosting services I've seen, where models are spun up behind containers, this service compiles the model down to either a static C library or WASM library, and then has a template lambda function do the HTTP parsing / argument handling / etc. Upsides of this approach: - You can directly embed models in applications, callable via FFI - You can run ML in weird places, like mobile apps, embedded devices, or the browser Downsides: - I have to implement the model inference for each algorithm - As such algorithm support is limited right now My goal is to make the interface easy enough that anyone who can build a model can use it to deploy their model somewhere it can deliver value, rather than having to request an engineer's help. I'm fine hosting models for now, but I'm also excited for building out custom integrations like running the models on mobile apps, inside existing applications via FFI, or even embedded devices. Please let me know what you think!

Visit product View on Hacker NewsJun 23, 2023

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

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

79%79% 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

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

TrustMRRFits verified-revenue profile · Strong signals: mobile apps, apps, video · Missing: ios, personal, entrepreneurs

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface, calls · Missing: plus, platform, intuitive

42%42% 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

17%17% 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.

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