Kubernetes and Artificial Intelligence (AI)

This intensive course explores the intersection of Kubernetes, a container orchestration platform, and Artificial Intelligence, focusing on how Kubernetes can be leveraged to effectively deploy, manage, and scale AI workloads in production environments.

Target Audience:

• Data Scientists

• Machine Learning Engineers

• DevOps Engineers

• Kubernetes Administrators

• Anyone interested in deploying and managing AI models at scale

Course Objectives:

• Understand the core concepts of Kubernetes and its role in container orchestration.

• Explore the challenges and considerations for deploying AI workloads in production.

• Learn how to leverage Kubernetes for containerizing AI models and serving them as APIs.

• Gain practical skills in managing AI pipelines with Kubernetes and related tools.

• Understand best practices for monitoring and scaling AI models in Kubernetes deployments.

Course Structure:

The course will be delivered in a blended format, combining lectures, hands-on labs, and discussions. Lectures will introduce key concepts and best practices, while hands-on labs will provide students with practical experience deploying and managing AI models with Kubernetes. Discussions will encourage critical thinking and problem-solving around real-world AI deployment scenarios.

Course Duration: 4 days

Module 1: Introduction to Kubernetes

• Fundamentals of containerization (Docker).

• Introduction to Kubernetes architecture (control plane, nodes, pods, etc.).

• Kubernetes deployment strategies (Deployments, ReplicaSets, etc.).

• Managing services and exposing applications (Services, Ingress).

• Hands-on Lab 1: Setting Up a Local Kubernetes Cluster (using Minikube or Kind)

• Hands-on Lab 2: Deploying a Simple Containerized Application in Kubernetes

Module 2: AI for Kubernetes

• Machine Learning lifecycle and its stages.

• Challenges of deploying AI models in production.

• Benefits of using Kubernetes for AI workloads.

• Introduction to AI frameworks and their containerization considerations (TensorFlow, PyTorch, etc.)

Module 3: Containerizing and Serving AI Models

• Techniques for containerizing AI models (Dockerfiles, dependencies).

• Leveraging container registries for storing and managing model images.

• Model serving frameworks for deploying models as APIs (TensorFlow Serving, KServe).

• Exposing AI models as Kubernetes services for consumption by applications.

• Managing model versions and rollouts with Kubernetes mechanisms.

• Hands-on Lab 3: Containerizing a Simple Machine Learning Model

• Hands-on Lab 4: Deploying a Containerized Model as a Service in Kubernetes

Module 4: Monitoring and Scaling AI

• Monitoring metrics for model performance (accuracy, precision, etc.).

• Detecting model drift and performance degradation.

• Scaling AI models based on load with Kubernetes autoscaling features.

• Introduction to tools for AI monitoring in Kubernetes (e.g., Prometheus, Grafana).

• Hands-on Lab 5: Implementing Basic Model Monitoring with Prometheus and Grafana