This is a one-week detailed Oxford Training Centre course on the deployment of AI models using Docker and Kubernetes. In the case of ever-growing demand for scalability of AI solutions, mastering knowledge in containerization and orchestration is becoming a must-have skill for an AI professional. In this course, you’ll be guided through hands-on practice in deploying, managing, and scaling AI/ML models in a containerized environment using Docker and Kubernetes. By the end of this course, you will be able to automate and optimize the process of deploying AI into production with maximum performance and scalability.
Objectives and target group
Objectives
By the end of this course, you will be able to learn the basics of AI model deployment using Docker and Kubernetes;
- practically understand how to containerize AI models using Docker for efficient deployment; understand the role of Kubernetes in AI model orchestration and scaling;
- automate the deployment of AI models using Kubernetes to optimize workflows; and proficiently manage machine learning models in production using Docker containers.
- Understand best practices for AI deployment to provide scalable and reliable AI solutions.
- Advanced scaling of AI models by using Kubernetes to handle production workloads at scale.
- The implementation of the AI model deployment pipeline – how to integrate and keep updated.
Target Group
This course is ideal for:
- AI engineers and data scientists looking to enhance their skills in AI model deployment.
- Machine learning engineers interested in automating and scaling AI solutions in production environments.
- DevOps professionals and cloud architects involved in managing machine learning workloads.
- IT professionals who want to specialize in containerizing AI models and deploying them using Docker and Kubernetes.
- Anyone interested in learning how to implement AI and machine learning models in real-world production environments using container orchestration tools.
Course Content
Introduction to AI Model Deployment
In the first section of the course, we’ll explore the concepts of containerization and orchestration as applied to AI models. You will understand the importance of Docker for containerizing AI models and Kubernetes for managing and scaling these containers in production environments. You will learn the fundamentals of creating Docker containers for AI applications and why Kubernetes is an essential tool for scaling machine learning models.
Containerizing AI Models with Docker
- How to package machine learning models into Docker containers for seamless deployment.
- Best practices for setting up Docker containers for AI applications.
- How to integrate Docker with machine learning workflows for faster deployment.
- The advantages of using Docker containers for managing AI models in development and production environments.
Scaling AI Models with Kubernetes
- How Kubernetes orchestrates AI models in production environments.
- The process of deploying AI models in Kubernetes clusters.
- Techniques for scaling AI models using Kubernetes to handle large volumes of requests and data.
- How Kubernetes can automate AI deployment, ensuring reliability and scalability in production environments.
AI Deployment Best Practices and Automation
- How to implement an AI deployment pipeline with Docker and Kubernetes.
- Strategies for handling model versioning and ensuring consistency across environments.
- Best practices for deploying AI models in production with Docker containers and Kubernetes clusters.
- How to optimize AI model performance by fine-tuning containerization and