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Snowpark ML is a set of tools including SDKs and underlying infrastructure to build and deploy machine learning models. With Snowpark ML, you can pre-process data, train, manage and deploy ML models all within Snowflake, using a single SDK, and benefit from Snowflake’s proven performance, scalability, stability and governance at every stage of the Machine Learning workflow.
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## Key Components of Snowpark ML
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The Snowpark ML Python SDK provides a number of APIs to support each stage of an end-to-end Machine Learning development and deployment process, and includes two key components.
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### Snowpark ML Development [Public Preview]
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### Snowpark ML Ops [Private Preview]
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Snowpark MLOps complements the Snowpark ML Development API, and provides model management capabilities along with integrated deployment into Snowflake. Currently, the API consists of
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1. FileSet API: FileSet provides a Python fsspec-compliant API for materializing data into a Snowflake internal stage from a query or Snowpark Dataframe along with a number of convenience APIs.
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1. Model Registry: A python API for managing models within Snowflake which also supports deployment of ML models into Snowflake Warehouses as vectorized UDFs.
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If you don't have a Snowflake account yet, you can [sign up for a 30-day free trial account](https://signup.snowflake.com/).
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### Create a Python virtual environment
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Python 3.8 is required. You can use [miniconda](https://docs.conda.io/en/latest/miniconda.html), [anaconda](https://www.anaconda.com/), or [virtualenv](https://docs.python.org/3/tutorial/venv.html) to create a Python 3.8 virtual environment.
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Python version 3.8, 3.9 & 3.10 are supported. You can use [miniconda](https://docs.conda.io/en/latest/miniconda.html), [anaconda](https://www.anaconda.com/), or [virtualenv](https://docs.python.org/3/tutorial/venv.html) to create a virtual environment.
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To have the best experience when using this library, [creating a local conda environment with the Snowflake channel](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#local-development-and-testing) is recommended.
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### Install the library to the Python virtual environment
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