Hi LudicrousParrot69
A bit of background:
A Task is a job executed in the system (sometime it is an experiment training, sometime a controller like the pipeline). Basically everything process can be a task.
Specifically the pipeline controller itself (i.e. the process running the Bayesian optimization) is Task in the system (i.e. a job running). What it does (using the HyperParameterOptimizer) is cloning previously executed Tasks (e.g. training experiments), change their parameters and monitor their results. All the Tasks in system are monitored and can be queried from anywhere.
You can see how to clone and launch Tasks manually here:
https://github.com/allegroai/clearml/blob/master/examples/automation/task_piping_example.py

The end goal of these questions is how to programatically go from the task name for the latest run of the HP optimisation controlling task, get the task for the best experiment underneath it, and access its model and then serve it using some external tools

If you have the optimizer object you can do:
best_task_objects = an_optimizer.get_top_experiments(top_k=3)If you have the specific Task ID:
task = Task.get_task(task_id='task_id_here')