Add execute_eval_run example to Tutorial 5

[tstadel]

Proposed changes:

• Add section Storing results in MLflow to Tutorial 5

Status (please check what you already did):

• First draft (up for discussions & feedback)
• Final code

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[julian-risch]

I tested the changes and got the results stored here: https://public-mlflow.deepset.ai/#/experiments/698 that looks good to me! 👍 @tstadel I will go ahead and merge this PR but I'll also leave some comments here on how I think we could improve the tutorial further.

One thing I noticed is that the comparison of scores returned by pipeline.eval(add_isolated_node_eval=True) and by reader.eval() is difficult.

reader.eval output is:

Reader Top-4-Accuracy: 99.09208819714657
Reader Top-1-Exact Match: 95.71984435797665
Reader Top-1-F1-Score: 95.73510337987335
Reader Top-4-Accuracy (without no_answers): 72.0
Reader Top-4-Exact Match (without no_answers): 44.0
Reader Top-4-F1-Score (without no_answers): 59.71344537815126

and pipeline.eval output is:

0.48 #print(metrics["Reader"]["exact_match"])
0.6027426153741944 #print(metrics["Reader"]["f1"])

Here, we could add a sentence to explain that the "without no_answers" metrics are the ones that a re expected to be similar.
As another small improvement, we could add a sentence in the tutorial after the headline "Run experiments". We should briefly explain here that an experiment consists of several executions of pipeline.eval() (evaluation runs) so that the user knows what to expect.