- Introduction to Microsoft SQL Server on Red Hat
Overview of SQL Server capabilities
Introduction to Red Hat Enterprise Linux and OpenShift
Understanding machine learning workloads in SQL Server
- Setting Up SQL Server on Red Hat Enterprise Linux
Installation and configuration of SQL Server on RHEL
Essential RHEL commands for managing SQL Server
Performance tuning and optimization in RHEL
- In-Database Machine Learning with SQL Server
Overview of SQL Server Machine Learning Services
Implementing R and Python scripts in SQL Server
Data preparation and transformation using SQL Server
- Building and Optimizing ML Pipelines
Designing efficient machine learning workflows
Model training and evaluation within SQL Server
Leveraging SQL Server for data ingestion and processing
- Containerizing SQL Server for Machine Learning
Introduction to containers and container orchestration
Deploying SQL Server on OpenShift
Best practices for containerized machine learning workloads
- Deploying Machine Learning Models on OpenShift
Continuous integration and deployment for ML models
Creating and managing OpenShift deployments for ML solutions
Monitoring and scaling ML applications on OpenShift
- Security and Maintenance
Implementing security best practices for SQL Server and OpenShift
Regular maintenance tasks for databases and containers
Backup and recovery strategies for SQL Server on Linux
- Case Studies and Real-World Applications
Exploring real-world scenarios using SQL Server ML workloads
Success stories and industry applications
Lessons learned and future trends in SQL Server ML on RHEL
- Final Project
Design, implement, and deploy a complete ML pipeline using SQL Server on RHEL and OpenShift
Presentation and peer review of final projects