Integrating AI with Kubernetes

*THIS IS A PRIVATE ONLY COURSE - CONTACT FOR PRICING* This intensive 4-Part course is designed for IT professionals, DevOps engineers, and AI practitioners who are keen to integrate AI applications with Kubernetes for scalable, robust, and efficient deployment. As the use of artificial intelligence (AI) continues to expand across industries, the need for managing AI workloads efficiently in production environments is critical. Kubernetes, as the leading container orchestration platform, offers an ideal framework to deploy, scale, and monitor AI applications.

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Course Days: 4


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Through a combination of lectures, hands-on labs, and interactive sessions, participants will gain in-depth knowledge of Kubernetes fundamentals, explore best practices for deploying AI models on Kubernetes, and learn how to optimize and monitor machine learning (ML) and deep learning (DL) workloads in real-world settings. By the end of this course, participants will be equipped with the skills to successfully deploy and manage AI applications on Kubernetes and harness the full potential of this powerful integration. 

 

Learning Objectives: 

By the end of the course, participants will be able to: 

  1. Understand Kubernetes Fundamentals: Grasp essential Kubernetes concepts and architecture, including containers, pods, and services, as well as Kubernetes clusters and nodes. 
  1. Deploy AI Models on Kubernetes: Learn how to containerize AI models and deploy them effectively on Kubernetes for scalable production workloads. 
  1. Optimize and Scale AI Workloads: Use advanced Kubernetes features, such as autoscaling and GPU acceleration, to optimize the performance of machine learning and deep learning models. 
  1. Monitor and Maintain AI Workflows: Implement tools and best practices for monitoring AI workloads in Kubernetes, ensuring stability, scalability, and efficient resource usage. 
  1. Leverage MLOps with Kubernetes: Understand MLOps concepts and integrate CI/CD pipelines to automate the model deployment, monitoring, and lifecycle management within Kubernetes. 
  1. Secure and Maintain Kubernetes Clusters: Gain insights into security considerations for deploying AI applications on Kubernetes and learn strategies for cluster maintenance and resource management. 

Learning Objectives: 

By the end of the course, participants will be able to: 

  1. Understand Kubernetes Fundamentals: Grasp essential Kubernetes concepts and architecture, including containers, pods, and services, as well as Kubernetes clusters and nodes. 
  1. Deploy AI Models on Kubernetes: Learn how to containerize AI models and deploy them effectively on Kubernetes for scalable production workloads. 
  1. Optimize and Scale AI Workloads: Use advanced Kubernetes features, such as autoscaling and GPU acceleration, to optimize the performance of machine learning and deep learning models. 
  1. Monitor and Maintain AI Workflows: Implement tools and best practices for monitoring AI workloads in Kubernetes, ensuring stability, scalability, and efficient resource usage. 
  1. Leverage MLOps with Kubernetes: Understand MLOps concepts and integrate CI/CD pipelines to automate the model deployment, monitoring, and lifecycle management within Kubernetes. 
  1. Secure and Maintain Kubernetes Clusters: Gain insights into security considerations for deploying AI applications on Kubernetes and learn strategies for cluster maintenance and resource management. 


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