What’s the general pattern for running a pipeline - train model, evaluate metrics and publish the model if satisfactory (based on a threshold, for example)

Basically I would do:
parameters for pipeline:
TaskA = Training model Task (think of it as our template Task)
Metric = title/series/sign we want to choose based on, where sign is max/min
Project = Project to compare the performance so that we could decide to publish based on the best Metric.

Pipeline:
Clone TaskA Change TaskA arguments (if needed) Launch and wait until completed Get TaskA's instance Metric value = Task.get_task(task_id='instance_id_111').get_last_scalar_metrics[Metric.title][Metric.series][Metric.sign])5. Get all Tasks with metric above/below this one,
tasks = Tasks.get_tasks(project=, name=, etc...) tasks = sorted(tasks, key=lambda x: x.get_last_scalar_metrics[Metric.title][Metric.series][Metric.sign]))6. pick the best one
# best task, if this is us, publish if tasks[-1].id == instance_id_111: tasks[-1].publish()wdyt?