[HN Gopher] Show HN: Otto-m8 - A low code AI/ML API deployment P...
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       Show HN: Otto-m8 - A low code AI/ML API deployment Platform
        
       Hi all, so I've been working on this low to no code platform that
       allows you to spin up deep learning workloads(I'm talking LLM's,
       Huggingface models, etc), interconnect a bunch of them, and deploy
       them as API's.  The idea essentially came up early in September,
       when experimenting with combining a Huggingface based BERT model
       with an LLM at work, and I realized it would be cool if I could do
       that instantly(especially since it was a prototype). At the time, I
       was considering a platform that could essentially help you train
       deep learning models without any code. It was my observation that
       much of the code required to train or even run inference on HF
       models have matured significantly. But before I solved that
       problem, I wanted to solve inference. Initially inspired by n8n and
       AWS Cloudformation, I built out otto-m8 (translates to automate).
       Given a json payload that lists out all the resources, and how each
       model is interconnected, launch it as one-off API the user can
       query. And thanks to Reactflow, the UI was just something I
       couldn't just not implement. And as I built it out, I did not want
       to miss out on the LLM and Agent bit.  With otto-m8, today, you can
       launch complex workflows by interconnecting HF models and
       LLM's(currently it supports OpenAI and Ollama only). But I like to
       see it being more than that. At the core, every workflow is an
       input process output model. Inputs get processed and there's an
       output. Therefore, with the way things are setup, one can integrate
       almost anything and make it interconnectable.  Project Link:
       https://github.com/farhan0167/otto-m8  Let me know what you guys
       think. I really would love feedback!
        
       Author : farhan0167
       Score  : 4 points
       Date   : 2024-12-21 21:39 UTC (2 days ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
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