Introduction

As machine learning (ML) and data science applications grow in complexity, Kubernetes has become the go-to solution for scalability, orchestration, and deployment in production environments. This course provides a comprehensive, hands-on approach to managing data science workflows using Kubernetes (K8s).

Participants will learn how to deploy and scale machine learning models, orchestrate data pipelines, and manage distributed workloads using Kubernetes. The course also covers MLOps best practices, Kubernetes-native tools (Kubeflow, Argo Workflows), and cloud-based deployments.

Course Objectives

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

• Understand the fundamentals of Kubernetes and its role in data science.
• Deploy and scale machine learning models and data processing pipelines on Kubernetes.
• Work with Kubernetes-native ML tools like Kubeflow, Argo Workflows, and MLflow.
• Implement GPU acceleration for deep learning workloads in Kubernetes clusters.
• Deploy containerized applications and manage CI/CD pipelines for ML models.
• Leverage cloud-native Kubernetes services (AWS EKS, Google GKE, Azure AKS).
• Apply MLOps strategies for model monitoring, retraining, and automation.

Who Should Attend?

This course is ideal for:

• Data scientists & ML engineers looking to scale and deploy models.
• Software engineers working with AI-driven applications.
• DevOps & MLOps professionals managing ML workloads in production.
• Cloud architects designing scalable data science solutions.
• Data engineers working with distributed data processing pipelines.

Day-by-Day Course Breakdown

Day 1: Introduction to Kubernetes for Data Science

Understanding Kubernetes Basics

• Introduction to Kubernetes architecture: Pods, Nodes, Deployments, Services.
• Why use Kubernetes for data science workflows?
• Setting up a local Kubernetes cluster using Minikube & K3s.

Deploying and Managing Containers in Kubernetes

• Working with Kubernetes manifests (YAML files).
• Deploying containerized data science applications using kubectl.
• Hands-on lab: Deploying a Jupyter Notebook on Kubernetes.

Day 2: Scaling Machine Learning & Data Pipelines

Managing Compute Resources for ML Workloads

• Configuring CPU & GPU workloads in Kubernetes.
• Optimizing ML training with horizontal & vertical scaling.
• Hands-on lab: Scaling TensorFlow & PyTorch models with Kubernetes.

Building & Orchestrating Data Science Pipelines

• Using Kubernetes Jobs & CronJobs for scheduled ML training.
• Hands-on lab: Automating an ETL pipeline with Kubernetes Jobs.

Day 3: Kubernetes for ML Model Deployment

Deploying and Serving ML Models in Kubernetes

• Introduction to model serving with TensorFlow Serving & FastAPI.
• Working with Kubernetes Ingress for API exposure.
• Hands-on lab: Deploying a containerized ML model as an API.

Implementing CI/CD for ML with Kubernetes

• Integrating GitOps, Helm, and Kustomize for version-controlled deployments.
• Hands-on lab: Deploying an ML model with a CI/CD pipeline using ArgoCD.

Day 4: MLOps with Kubernetes & Kubeflow

Introduction to Kubeflow for Scalable ML

• Understanding Kubeflow Pipelines & Katib for Hyperparameter Tuning.
• Deploying Jupyter Notebooks, TensorFlow, and PyTorch jobs in Kubeflow.
• Hands-on lab: Building an ML pipeline with Kubeflow Pipelines.

Monitoring & Logging for Data Science Workloads

• Using Prometheus & Grafana for monitoring ML workflows.
• Hands-on lab: Setting up model monitoring in Kubernetes.

Day 5: Cloud Deployments & Advanced Kubernetes Use Cases

Deploying Kubernetes Clusters on the Cloud

• Working with AWS EKS, Google Kubernetes Engine (GKE), Azure AKS.
• Auto-scaling Kubernetes clusters with cluster autoscaler.
• Hands-on lab: Deploying a data science workflow on GKE or EKS.

Capstone Project: End-to-End Kubernetes Data Science Workflow

• Participants will design, deploy, and optimize a complete ML pipeline using Kubernetes.
• Model training, deployment, monitoring, and scaling.
• Final presentations and peer reviews.

Conclusion & Certification

At the end of the course, participants will receive a Certificate of Completion, demonstrating their expertise in Data Science Workflows with Kubernetes.

This course combines hands-on labs, real-world case studies, and best practices, preparing learners for scalable, production-grade AI deployments in Kubernetes.