[Gluber]

Senior Engineer, semi retired.

Working in my spare time on various side projects, but i would really enjoy some input of which project to focus on, collaborate on and potentially incorporate.

1. Digital Assistant for on premise installation, already built that as lead architect for a customer of mine but want to take the concept fruther ( think star trek TNG computer )

2. Continous integration platform for ML Models, taking data collection, labelling and training as well as optimization and automate them as much as possible.

[kinow]

I think for your second item you should find a lot of things if you search "MlOps"

- https://twitter.com/hashtag/mlops

I co-mentored GSoC a couple years ago to write a Jenkins Machine Learning plug-in that tries to integrate Jenkins CI & Jupyter/Zeppelin. At that time there were already tools & standards appearing.

If I recall correctly, Netflix had some blog posts about their ML pipeline. In one of them I remember reading about their workflow tools, some that could be used for CI/CD style development of ML models.

[Gluber]

I think i should elaborate a bit more. ( i am pharaphrasing what i've seen/built for customers working on real world ML projects )

Let assume for a task of building a vision model like tesla for autonomous driving, basically taking camera feeds and turning them into 3D geometry.

For that you have to:

1. Collect Data

2. Curate Data

3. Augment Data ( i don't mean classic image augmentation techniques, but for example connecting a simulator to provide artifical data samples )

4. Label data

5. Define your model / or figure out a good model with presets/auto ml techniques

6. Train it at scale

7. Analyze/Test your model, think unit testing but for ml

8. Optimize it to run on edge hardware

9. Deploy it and distribute

All of that with proper A/B Testing, different models, and continously improving/tweaking and adding data.

THere are literally 100s of tools in that space, covering one or multiple steps. But nothing that integrates the whole. It still has an incredible lead time / engineering effort to setup / build a pipeline like that and run it at scale, handle the workflows behind it and also be able to run on premise ( using cloud resources for a lot of that is both a no go for many large companies due to security concerns and cost )

Some cloud SAAS software comes quite close e.g Google Vertex Ai, Sagemaker etc.. But they still fall very short for a production pipeline.

[no_circuit]

Cool. I'm also working on various side projects myself now too. Originally I thought would be in the general area near your -1- and -2-, but ended up finding myself spending my time learning general devops and web dev for the first time. I've worked on something like -1- before, but felt it would take too long to put together without the backing of a large amount of funding for data collection/generation and some collaborators as I'm a generalist.

I think I'll eventually get to ML CI, whether as part of a startup, or back in regular employee life.