Add PyTorch DistilBERT Sentiment Analysis Batch/Streaming pipelines for ML Benchmarks

[Amar3tto]

Changes:

1. Added new pipeline PyTorch DistilBERT Sentiment Analysis streaming to the beam_Inference_Python_Benchmarks_Dataflow workflow.
2. Added new pipeline PyTorch DistilBERT Sentiment Analysis batch to the beam_Inference_Python_Benchmarks_Dataflow workflow.
3. Improved ML pipelines metrics pages on the website.

Successful run: https://github.com/Amar3tto/beam/actions/runs/14516082090/job/40725350012

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[damccorm]

I think we need to add a few things to this:

1. These should run as separate pipelines and we should only monitor the results of the streaming portion
2. We need a way of applying a consistent rate to the input elements in pub/sub. A better approach might be to either (a) just do this independently via a script, or (b) within Dataflow use periodic impulse or state/timers to control the rate at which we emit. This will likely work better as a helper which we can use across pipelines
3. For streaming pipelines, we should enable autoscaling

[Amar3tto]

Added autoscaling.
Can we merge this PR with the current approach (it is the easiest way to run two pipelines simultaneously)?
Then we can implement reusable independent script that controls input rate.

[damccorm]

I think we should at least handle (1) and run these as two pipelines. Otherwise, I do not think we are measuring a meaningful dataset

[Amar3tto]

Done

[github-actions]

Reminder, please take a look at this pr: @liferoad @Abacn @liferoad

[liferoad]

@jrmccluskey please review this PR. Thanks.

[jrmccluskey]

As best I can tell @damccorm's comments haven't been addressed yet so I'll defer here

[github-actions]

Reminder, please take a look at this pr: @liferoad @Abacn @liferoad

[damccorm]

waiting on author

[damccorm]

Thanks!