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CODE: NAI11
DURATION: 3 Days | 5 Days | 10 Days
CERTIFICATIONS: CPD
This course provides practical training on how to deploy AI and machine learning models using Docker and Kubernetes. Participants will learn the fundamentals of containerization, building Docker images, and managing containerized applications with Kubernetes. The course covers best practices for packaging, scaling, and maintaining AI models in production environments. Delegates will acquire the necessary skills needed to automate deployment workflows, ensure model reliability, and manage infrastructure for scalable AI solutions.
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:
Explain how containerization works using Docker.
Create and manage Docker containers for AI and ML models.
Set up Kubernetes clusters to run and manage model deployments.
Deploy, monitor, and scale AI applications effectively.
Automate model deployment using CI/CD pipelines.
Identify and resolve common deployment issues.
Apply best practices for securing and maintaining deployed models.
Build scalable and reliable AI workflows for real-world environments.
At the end of this course, you’ll understand:
This course is ideal for machine learning engineers, data scientists, DevOps professionals, software developers, and AI practitioners who want to deploy and scale AI models in real-world environments. It is also an important course for IT infrastructure teams and technical project managers involved in managing AI workloads. Participants should have basic knowledge of Python, machine learning concepts, and familiarity with command-line tools.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
