CODE: AI20
DURATION: 3 Days/5 Days/10 Days
CERTIFICATIONS: CPD
Scaling artificial intelligence from experimentation to production requires a robust, efficient, and scalable infrastructure. This course provides the essential knowledge and practical skills to design, implement, and manage the complete technology stack that powers enterprise AI. It covers key components such as GPU, CPU, and DPU hardware, along with networking and storage essentials for AI workloads. Participants will learn to navigate the complex landscape of hardware, cloud services, and orchestration tools to build a foundation that supports the entire machine learning lifecycle from data processing and model training to deployment and monitoring while optimizing for performance, cost, and reliability.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Design and provision scalable compute infrastructure for training and inference workloads.
Implement and manage GPU-accelerated computing environments efficiently.
Select and configure optimal storage solutions for various data and model types.
Orchestrate AI workloads using containerization and Kubernetes.
Automate MLOps pipelines for continuous integration and delivery.
Implement monitoring, logging, and cost optimization strategies.
Ensure security, governance, and compliance across AI infrastructure.
At the end of this course, you’ll understand:
This course is designed for technology professionals responsible for building and maintaining AI capabilities, including ML infrastructure engineers, DevOps and MLOps engineers, cloud architects and solutions engineers, IT infrastructure managers, and data platform engineers seeking practical skills to design, deploy, and manage scalable AI systems.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
