Latest update [2.232.2] introduces MLFlow

[mathcass]

Describe the bug
The latest update to the SageMaker SDK introduces sagemaker-mlflow which then brings in a constellation of dependencies that users might not need. This is a minor change (v2.232.1...v2.232.2) that leads to installing 30 new dependencies.

To reproduce
Install the latest update, pip install sagemaker==2.232.2.

Expected behavior
I would have liked if this release had been included via Python "extras" where you can specify an additional set of installation requirements. For example, rather than installing sagemaker-mlflow automatically, only install it if someone specifies `pip install "sagemaker[mlflow]>=2.232.2".

Screenshots or logs
If applicable, add screenshots or logs to help explain your problem.

System information
A description of your system. Please provide:

• SageMaker Python SDK version: 2.232.2
• Framework name (eg. PyTorch) or algorithm (eg. KMeans): N/A
• Framework version: N/A
• Python version: N/A
• CPU or GPU: N/A
• Custom Docker image (Y/N): N

Additional context
The package could be more usable and leaner if it implemented this as a Python "extra".

[jgangelBLNG]

I also ran into this when a deploy failed unexpectedly. Looks like there was a request to make it optional that wasn't addressed before merge - #4880 (comment)

Edit: mlflow actually puts us over the lambda max deployment size so I'll need to avoid this version entirely until this is made optional

[ryansteakley]

#4903 open for review

[benieric]

Hi @mathcass, thanks for reaching out about this. We have merged the fix in this PR - #4903

Should hopefully be resolved on next release

[aaravind100]

hey can confirm, this is fixed in v2.234.0+

thank you!!

[benieric]

Closing since has been resolved in this PR - #4903

[mathcass]

I saw this but I'm late to coming back to comment on it. Thank you so much for addressing and responding to the issue @ryansteakley & @benieric. I appreciate you.