AutoML experiment: get model and metrics for any algorithm (not only for the best one)

## What I'm trying to do

For an **AutoML Forecasting** experiment, I'd like to compare the performance of the **best model** with the performance of **another model** from the same experiment.

For an AutoML run, I understand how to get the best performing model and its metrics like this:
```
# ...initialize MLFlow client...
mlflow_parent_run = mlflow_client.get_run('upbeat_square_abs3942')
best_child_run_id = mlflow_parent_run.data.tags["automl_best_child_run_id"]
best_run = mlflow_client.get_run(best_child_run_id)
best_run.data.metrics
# etc...
```

But how can I fetch the job for _any_ model based on the _algorithm name_?
Something like:
```
# pseudocode:
mlflow_client.get_automl_run_by_algorithm('XGBoostRegressor')
```

## So far, I managed to figure out the following:

1. list of algorithms used in the AutoML experiment
```
mlflow_parent_run.data.tags['pipeline_id_000']
# '__AutoML_Naive__;__AutoML_SeasonalNaive__;__AutoML_Average__;__AutoML_SeasonalAverage__;__AutoML_Ensemble__'
```
However, this list seems to be in an arbitrary order and I struggle to get the corresponding job names for the algorithms.

2. "internal" job names for the child runs

The child runs seem to have different names than the names shown in Azure ML Studio.
They are named for instance `upbeat_square_abs3942_2` - i.e. the name of the parent run `upbeat_square_abs3942` followed by **underscore plus a number** (`_2`in this example).

But Azure ML Studio displays names like (no `upbeat_square_abs3942_2` to be found):

So this code works:
```
child_run = mlflow_client.get_run('upbeat_square_abs3942_2')
```
but using a name shown in the screenshot above throws an exception, e.g.
```
child_run = mlflow_client.get_run('green_floor_0ln3tlpv')
```

### Question

How can I obtain the model and metrics for any algorithm used in the experiment?

Thanks!

Comece pelo uso do cliente MLflow mostrado na issue, incluindo get_run, a tag automl_best_child_run_id e pipeline_id_000. Rastreie como as execuções filhas do AutoML são identificadas e como os nomes dos algoritmos se relacionam com seus nomes internos de execução. O trabalho estará concluído quando a recuperação de um modelo e suas métricas para qualquer algoritmo do experimento estiver documentada ou habilitada, não apenas para a melhor execução.

Escrita pelo modelo de indexação a partir do texto da issue.