GoTaglio is a lightweight python toolbox for creating ML pipelines for model evaluation and case labeling. Its goal is to accelerate the Applied Science inner-loop by allowing principled experimentation to start informally on an engineer's machine in minutes, while producing learnings and artifacts that scale through production.

GoTaglio is designed to be very low friction. It is kind of like a thumb drive, loaded with power tools, that will work in any Python environment.

• It does not require significant cloud infrastructure deployment. All that is needed are model endpoints and credentials to access them.
• It can be used in cloud environments like AzureML or with frameworks like mlflow.
• Pipeline code can be incorporated into production systems.

GoTaglio includes the following key elements:

• Ability to rapidly define and run end-to-end ML pipelines.
• Automatic logging and organization of information about runs.
• The ability to rerun an earlier experiment with small changes introduced on the command-line.
• Structured logging to facilitate run analysis, comparing runs and tracking key metrics over time as the pipeline evolves.
• A python library that can be accessed from Jupyter notebooks.
• A command-line tool to simplify common operations.
• [COMING SOON] A web-based tool for oragnizing and labeling cases.

GoTaglio comes with several samples that run out-of-the-box with included LLM mocks or your LLM endpoints.

Get an overview of key GoTaglio concepts such as

• configuration merging
• models
• pipelines
• structured logging

Learn how to incorporate GoTaglio into your process as

• a command-line tool
• a Jupyter notebook enhancement
• a python library