Show HN: Build Machine Learning Web-Service with Python and Django

Calling the ML model should be done via Django Channels or a similar background queue and not directly within the view (request handler) as if the inputs/inference is too big, it will block and cause issues for load handling.

For anyone looking to deploy ML models a little bit easier, check out <a href="https:&#x2F;&#x2F;inferrd.com" rel="nofollow">https:&#x2F;&#x2F;inferrd.com</a><p>It&#x27;s a simple drag and drop to deploy tensorflow, scikit, spacy, keras or pytorch.

A Django app for managing training data and active labeling would be a nice complement to this. Something similar to AWS GroundTruth.<p>We should start thinking about an ML life cycle were data is ingested, data labeled labeled, models trained, model tested, model deployed and monitored. Rinse, lather, repeat.

For anyone who is looking to make deploying ML models easier, I created <a href="https:&#x2F;&#x2F;deepserve.ai" rel="nofollow">https:&#x2F;&#x2F;deepserve.ai</a><p>You can run `deepserve deploy` on the command line and it will stand up a REST endpoint for your model. I&#x27;m also building client side SDKs to make it easier to call on these models in your application code.<p>It&#x27;s still in beta but feel free to reach out to me jeff @ deepserve.ai

[Gradio](gradio.app) can save a lot of time to serve the same purpose - making it super easy to serve any python based ML. It provides dozens of interface components, including text, image, audio, files, and dataframes to support many different types of predictions.

I noticed that the models use CharField with a max_length of 10K. Why not use TextField?

Why use Django when Flask is so much simpler and has less overhead?<p>For just a REST ML model endpoint, Flask does everything you need, and no more. It&#x27;s a perfect fit.