SQL Server Machine Learning Services: Python

2h 32mAdvanced2023-12-21

Authors

Adam Wilbert

Data Visualization Expert

Course details

Learn how to analyze SQL Server data with Python. Database expert Adam Wilbert shows how to use a powerful combination of tools, including high-performance Python libraries and the Machine Learning Services add-on, directly inside SQL Server to streamline analysis. Adam shows how to use Python scripts to perform statistical analysis, generate graphics such as scatterplots and bar charts, and process tabular data. He also explains how to turn a Python script into a stored procedure and set up standalone ML services to execute scripts without impacting SQL Server performance.

Topics include:
- Analyzing SQL Server data with Python
- Installing Machine Learning Services
- Writing Python scripts for SQL Server
- Python packages and libraries
- Producing graphics with Matplotlib
- Processing tabular data
- Creating a SQL stored procedure
- Creating an external data science client

Skills covered

SQL ServerDatabase AdministrationMachine LearningPythonDatabase ManagementArtificial Intelligence (AI)Programming LanguagesOpen SourceMicrosoftSoftware DevelopmentDeep Dive (X:Y)

Concepts

0. Introduction

01 - Analyze SQL Server data with Python
02 - What you should know
03 - Using the exercise files

1. Get Started with MLS

04 - What is machine learning services
05 - Install ML services for Python
06 - Enable script execution in SQL Server
07 - Use variables in Python
08 - Create a Python while loop

2. Write Python Scripts for SQL Server

09 - Import a dataset from SQL Server
10 - Manipulate a data frame
11 - Output a result set to SQL Server
12 - Python syntax pitfalls
13 - Challenge - Import a data frame
14 - Solution - Import a data frame

3. Python Package Modules and Libraries

15 - The Anaconda open-source packages
16 - Functions in the revoscalepy package
17 - Model, train, and score with microsoftml
18 - Produce graphics with MatPlotLib
19 - Get descriptive statistics with pandas
20 - Challenge - Sample a data frame
21 - Solution - Sample a data frame

4. Processing Tabular Data

22 - Return values with indexes and series
23 - Convert a series to a data frame
24 - Add multiple series to a data frame
25 - Include the index in a data frame
26 - Slice a data frame to series
27 - Challenge - Import and process data
28 - Solution - Import and process data

5. Create a SQL Stored Procedure

29 - Create a Python stored procedure
30 - Parameterize the procedure
31 - Challenge - Write a stored procedure
32 - Solution - Write a stored procedure

6. Create an External Data Science Client

33 - Install MLS on a standalone server
34 - Add development tools to the client
35 - Work with Jupyter Notebooks

Conclusion

36 - Next steps