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[D] Good place to download pre-trained GANs?

Main Post:

Looking for GANs that output let's say 128x128, 256x256 or 512x512 images. I found a BIGGAN 128 model, but I wonder if someone has put these together in an organized fashion somewhere?

Top Comment: BigGAN has 128,256,612 pretrained models - https://tfhub.dev/s?q=biggan

Forum: r/MachineLearning

[D] AI Market place - Sell your models?

Main Post:

I'm proving out a container marketplace concept and wanted the feedback of you guys to see if there is any willingness to sell machine learning models wrapped up in (docker) containers on a marketplace. Just like rapidAPI but for docker containers.

The thesis is that there are so many amazing models out there, why would you pay amazon or google per api request if you could just as easily spin one up on their platform and have it cost you far less (especially if you are hitting it a lot).

The second insight was edge computing. API's in the cloud are great unless your network is garbage or you're transporting so much data (video perhaps) over the wire that it becomes cost prohibitive to do the machine learning in the cloud.

Anyways before putting any more time into the concept, I'd love to get some thoughts.

What I have so far is at https://sugarkubes.io.

Top Comment: I'm not sure if there is a market for buying models, probably the people/companies with the money to spend have specific problems they need solved and an off-the-shelf model isn't good enough (or put another way, applying ML in business is more about managing data/training/deployment than individual models).

Forum: r/MachineLearning

[D] Scrum has no place in data science

Main Post:

I've seen more and more teams lately hopping on the Scrum bandwagon so I decided to write a post detailing why this is a bad idea. In summary, my objections primarily fall into four categories:

The uncertainty in data science makes point estimates essentially worthless.
Sprints are too short to deliver meaningful results. As a result data scientists often sacrifice documentation, code quality, or model robustness to meet the arbitrary deadline.
The product owner has too much direction in the determining the backlog and this often results overlooking important issues.
Grooming and other sessions gradually begin to take up more and more time, due to the aforementioned issues (e.g. we need more grooming because haven't delivered stories in the last sprint).

Top Comment:

I think one of the determining factors is where your data science team is in its growth cycle. If you're in a data first company, with plenty of deployed models that need periodic updates and have defined business questions, then Scrum can be a great option.

That said, very few companies are in that stage of their development.

Personally my biggest concerns with scrum is lack of free form time that can be used to innovate and explore, as well as the burnout that can come from an overly aggressive sprint schedule - not sustainable for the amount of mental bandwidth data science requires.

Personally, I pick and choose components from Scrum to manage my data science teams and its worked well, but all scrum all the time would be disastrous IMO.

Forum: r/datascience