SQL Server Machine Learning Services: Python

Introduction

• Analyze SQL Server data with Python
• What you should know
• Using the exercise files

1. Get Started with MLS

• What is machine learning services?
• Install ML services for Python
• Enable script execution in SQL Server
• Use variables in Python
• Create a Python while loop

2. Write Python Scripts for SQL Server

• Import a dataset from SQL Server
• Manipulate a data frame
• Output a result set to SQL Server
• Python syntax pitfalls
• Challenge: Import a data frame
• Solution: Import a data frame

3. Python Package Modules and Libraries

• The Anaconda open-source packages
• Functions in the revoscalepy package
• Model, train, and score with microsoftml
• Produce graphics with MatPlotLib
• Get descriptive statistics with pandas
• Challenge: Sample a data frame
• Solution: Sample a data frame

4. Processing Tabular Data

• Return values with indexes and series
• Convert a series to a data frame
• Add multiple series to a data frame
• Include the index in a data frame
• Slice a data frame to series
• Challenge: Import and process data
• Solution: Import and process data

5. Create a SQL Stored Procedure

• Create a Python stored procedure
• Parameterize the procedure
• Challenge: Write a stored procedure
• Solution: Write a stored procedure

6. Create an External Data Science Client

• Install MLS on a standalone server
• Add development tools to the client
• Work with Jupyter Notebooks

Conclusion

• Next steps