Machine Learning with SQL Server - From the Edge to the Cloud

Overview

Explore machine learning options in SQL Server ecosystem, from edge devices to cloud, covering training, testing, and operationalization for various workloads and design patterns.

Syllabus

• Introduction to Machine Learning and SQL Server
Overview of SQL Server Ecosystem
Introduction to Machine Learning Concepts
• Installation and Configuration
Setting Up SQL Server for Machine Learning
Installing Required Extensions and Tools
• Machine Learning with SQL Server on Edge Devices
Overview of Edge Computing
Implementing ML Models on Edge Devices
Case Studies and Use Cases
• Data Preparation in SQL Server
Data Extraction Techniques
Data Cleaning and Transformation with T-SQL
Feature Selection and Engineering
• Model Training and Testing
Training ML Models Using SQL Server Machine Learning Services
Testing and Validating Models within SQL Server
Cross-Validation and Hyperparameter Tuning
• Operationalizing Machine Learning Models
Deploying Models to SQL Server
Automation and Scheduling of ML Workflows
Integrating with SQL Server Agent for MLOps
• Machine Learning in the Cloud with Azure
Overview of Azure Machine Learning
Integrating SQL Server with Azure Services
Building and Deploying Models in the Cloud
• Design Patterns for Machine Learning Workloads
Identifying Suitable ML Design Patterns
Implementation of Common ML Patterns in SQL Server
Case Studies and Practical Applications
• Monitoring and Managing ML Models
Performance Monitoring Using SQL Server Tools
Model Management and Lifecycle
• Security and Compliance
Understanding Security in SQL Server for ML
Data Privacy and Compliance Considerations
• Capstone Project
Designing an End-to-End Machine Learning Solution
Presentation and Review of Projects
• Recap and Future Directions
Summary of Key Concepts
Emerging Trends in SQL Server and Machine Learning

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Subjects

Conference Talks