SQL Server 2017 Python tutorials
This article describes the Python tutorials for in-database analytics on SQL Server 2017 Machine Learning Services.
- Learn how to wrap and run Python code in stored procedures.
- Serialize and save Python-based models to SQL Server databases.
- Learn about remote and local compute contexts, and when to use them.
- Explore the Microsoft Python modules for data science and machine learning tasks.
Python quickstarts and tutorials
|Quickstart: "Hello world" Python script in SQL Server||Learn the basics of how to call Python in T-SQL.|
|Quickstart: Create, train, and use a Python model with stored procedures in SQL Server||Explains the mechanics of embedding Python code in a stored procedure, providing inputs, and stored procedure execution.|
|Tutorial: Create a model using revoscalepy||Demonstrates how to run code from a remote Python terminal, using SQL Server compute context. You should be somewhat familiar with Python tools and environments. Sample code is provided that creates a model using rxLinMod, from the new revoscalepy library.|
|Tutorial: Learn in-Database Python analytics for SQL developers||This end-to-end walkthrough demonstrates the process of building a complete Python solution using T-SQL stored procedures. All Python code is included.|
These samples and demos provided by the SQL Server development team highlight ways that you can use embedded analytics in real-world applications.
|Build a predictive model using Python and SQL Server||Learn how a ski rental business might use machine learning to predict future rentals, which helps the business plan and staff to meet future demand.|
|Perform customer clustering using Python and SQL Server||Learn how to use the Kmeans algorithm to perform unsupervised clustering of customers.|