ml-platform

I am using metaflow locally but with the AWS service (e.g. the actual compute is happening locally rather than in AWS batch but the metadata is using AWS). When I access the run data through run.data I get new local directories with names like metaflow.s3.w3efey1k, which I presume is because metaflow pulls from S3 into that directory, and then un-pickles the result from there. Is there a way t

While reading the code I found the following (minor) issue.

The aiohttp docs states: app.make_handler() is deprecated and will be removed in future aiohttp versions. Please use Application runners ins

Currently you can only do training when you've committed and pushed all new changes in your branch. This introduces a blocker when a Data Scientist is trying out lots of different changes in their code.

Allow for training even with uncommitted changes. This can be done by taking a git diff of the current branch, storing it and then doing an git apply to the current branch during training

Add overview and map proper issues to it.