Machine Learning Operations with Kubernetes, Kubeflow, and GCP
Length: 6 Weeks

Course Description: This comprehensive course provides an in-depth examination of Machine Learning Operations (MLOps) using Kubernetes, Kubeflow, and GCP. Covering everything from the basics of MLOps, Kubernetes, and GCP, to complex model deployments using Kubeflow, the course will equip you with the skills needed to automate, deploy, and manage Machine Learning workflows. With hands-on exercises and practical case studies, you’ll learn how to implement efficient, scalable, and secure ML solutions in a production environment.

Module 1: Explain MLOps to management stakeholders
Description: In this module, we introduce the concept of MLOps, its role in the tech industry, and the lifecycle of a Machine Learning project. We’ll discuss the challenges typically encountered when implementing Machine Learning projects and lay the foundation for the remainder of the course.

Performance Objectives:

1. 1. Define the concept and role of MLOps in a presentation to management stakeholders
   2. Map out the lifecycle of a Machine Learning project in a flowchart for management stakeholders
   3. Identify key challenges in a provided ML project scenario and propose possible solutions for management stakeholders

Module 2: Get Management Buy-In for a Kubernetes and GCP Implementation
Description: Module 2 provides an overview of Kubernetes and GCP, focusing on their roles in managing scalable and resilient applications. You’ll learn how to install and configure both GCP CLI and Kubernetes, setting the stage for more advanced topics.

Performance Objectives:

1. Explain the concept and role of Kubernetes and GCP in managing infrastructure to management stakeholders {e.g., in an informal project proposal)
2. Create a diagram that outlines the core components of Kubernetes for management stakeholders
3. Successfully install and configure CLI and Kubernetes on your local machine

Module 3: Take a Deep Dive into Kubernetes and Integration with AWS
Description: In this module, we delve deeper into the core components of Kubernetes and how they interact with GCP. You’ll learn how to deploy and manage applications on Kubernetes using GCP, as well as how to handle networking, security, and storage. This module also includes training on monitoring and troubleshooting Kubernetes applications with GCP tools.

Performance Objectives:

1. Deploy a sample application on Kubernetes using GCP
2. Set up Kubernetes networking, security and storage for the deployed application
3. Monitor and troubleshoot the deployed Kubernetes application with GCP tools

Module 4: Install and Configure Kubeflow
Description: Module 4 is dedicated to Kubeflow, a key tool in MLOps for orchestrating Machine Learning workflows on Kubernetes. After explaining the concept and role of Kubeflow, you’ll get hands-on experience installing and configuring it on a Kubernetes cluster, and building a simple pipeline.

Learning Objectives:

1. Define the concept and role of Kubeflow in MLOps in a written report to management
   Install and configure Kubeflow on a Kubernetes cluster
2. Create a simple Kubeflow pipeline and explain its functionality

Module 5: Implement MLOps with Kubeflow on Kubernetes and GCP
Description: This module combines the knowledge and skills you’ve acquired so far to implement MLOps principles using Kubeflow, Kubernetes, and GCP. You’ll learn how to automate Machine Learning workflows, how to implement a model from training to serving, and how to handle model versioning and rollouts.

Learning Objectives:

1. Set up a CI/CD pipeline for a ML model using Kubeflow, Kubernetes, and GCP
2. Automate a provided ML workflow using Kubeflow Pipelines
3. Train, deploy, and serve a ML model using Kubeflow, Kubernetes, and GCP
   Implement model versioning and rollouts for the served model

Module 6: Implementing Advanced MLOps Techniques and Best Practices
Description: The final module covers advanced MLOps techniques, including handling complex use-cases with Kubeflow and best practices for logging, monitoring, and alerts for ML models in production. You’ll learn how to implement multi-tenancy and secure your ML workflows, with case studies and updates on the latest advancements in the MLOps field to ensure your skills are up-to-date.

Learning Objectives:

1. Implement a complex use-case (e.g., distributed training) using Kubeflow
2. Set up logging, monitoring, and alerts for your ML model in production
3. Implement multi-tenancy for your ML workflows on GCP and Kubernetes
4. Present a case study of MLOps implementation
   Perform and report on an analysis of how the latest advancements in MLOps they could apply to your own workflows