aws-samples / aws-samples/amazon-sagemaker-mlops-workshop
How can I tune the pipeline itself?
Open
- Dominant language
- Jupyter Notebook
- Stars
- 113
- Forks
- 32
- PR merge metrics
- No merged PRs in 30d
Description
Is it possible tune the the pipeline as a whole? i.e., find the best version of processing and training parameters (combined) for given input?
Contributor guide
Research direction
The issue does not name a file, notebook, test, or entry point. First clarify which processing and training parameters should be tuned together and what pipeline-tuning behavior and success criteria would count as done.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, jupyter-notebook
- Domain
- devops, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100