Microsoft SQL Server Machine Learning Workloads on Red Hat Enterprise Linux and Red Hat OpenShift

Overzicht

Accelerate machine learning pipelines using SQL Server on Red Hat Enterprise Linux and OpenShift. Learn in-database ML techniques and containerization for efficient model production and deployment.

Lesprogramma

• 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

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Vakgebieden

Conference Talks