# GoTaglio
`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](https://azure.microsoft.com/en-us/products/machine-learning) or with frameworks like [mlflow](https://mlflow.org/).
* 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](https://jupyter.org/).
* A command-line tool to simplify common operations.
* [COMING SOON] A web-based tool for oragnizing and labeling cases.
## Try GoTaglio
GoTaglio comes with [several samples](documentation/samples.md) that run out-of-the-box with included LLM mocks or your LLM endpoints.
## Learn GoTaglio
Get an overview of key [GoTaglio concepts](documentation/concepts.md) such as
* configuration merging
* models
* pipelines
* structured logging
## Use GoTaglio
Learn how to [incorporate GoTaglio into your process](documentation/usage.md) as
* a command-line tool
* a [Jupyter notebook](https://jupyter.org/) enhancement
* a python library
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"description": "# GoTaglio\n\n`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.\n\n`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.\n* It does not require significant cloud infrastructure deployment. All that is needed are model endpoints and credentials to access them.\n* It can be used in cloud environments like [AzureML](https://azure.microsoft.com/en-us/products/machine-learning) or with frameworks like [mlflow](https://mlflow.org/).\n* Pipeline code can be incorporated into production systems.\n\n`GoTaglio` includes the following key elements:\n* Ability to rapidly define and run end-to-end ML pipelines.\n* Automatic logging and organization of information about runs.\n* The ability to rerun an earlier experiment with small changes introduced on the command-line.\n* Structured logging to facilitate run analysis, comparing runs and tracking key metrics over time as the pipeline evolves.\n* A python library that can be accessed from [Jupyter notebooks](https://jupyter.org/).\n* A command-line tool to simplify common operations.\n* [COMING SOON] A web-based tool for oragnizing and labeling cases.\n\n## Try GoTaglio\n\nGoTaglio comes with [several samples](documentation/samples.md) that run out-of-the-box with included LLM mocks or your LLM endpoints.\n\n## Learn GoTaglio\n\nGet an overview of key [GoTaglio concepts](documentation/concepts.md) such as\n* configuration merging\n* models\n* pipelines\n* structured logging\n\n## Use GoTaglio\n\nLearn how to [incorporate GoTaglio into your process](documentation/usage.md) as\n* a command-line tool\n* a [Jupyter notebook](https://jupyter.org/) enhancement\n* a python library\n\n",
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