55335A - SQL Server Machine Learning using Python, R and Java
This lab intensive one-day course will help database administrators, data scientists and developers to utilize the full potential of SQL Server Machine Learning Services using Python, R, and Java. Participants will gain hands-on experience creating ML tasks on SQL Server to get more value from their data. The course is designed for SQL Server 2022 and newer versions. The format of the class can be Instructor-Led or Workshop.
Prerequisites
- A basic knowledge of SQL
- A basic knowledge of command-line scripting (PowerShell, Shell or Batch)
- A basic knowledge of one of the programming languages (Python, R or Java)
- Working knowledge of SQL Server database management
Audience
Data professionals who want to use ML tools on data residing in SQL Server databases. A basic knowledge of T-SQL and one of the
three programming languages used for the labs (Python or R or Java) is
recommended. This is a SQL Server Machine Learning course. Although programming
is necessary (and scripted) for some of the exercises, the focus of the
material is the ML capabilities of SQL Server.
Learning Objectives
After completing this course, students will be able to:
- Leverage Python, R, and Java for ML tasks within SQL Server
- Implement text analysis, natural language processing, and anomaly detection
- Develop image processing and computer vision solutions
- Create predictive analytics models integrated with SQL Server
- Apply time series forecasting techniques
- Optimize ML model performance within the database environment
- Implement security best practices for in-database ML services
- Understand the advantages of on-premises ML solutions
This course empowers data professionals to extract maximum value from their datasets
without the hassle of moving or copying data from its source. By learning to
use Python, R, or Java to perform machine learning tasks directly on the SQL
Server instance, students will unlock the full value of their data while
gaining security, performance and efficient workflow advantages.
What's included?
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4 Chapters
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4 Reviews
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4 Labs