Kubernetes Quest Next-Level Ml Engineering

Harnessing the Power of Kubernetes for Advanced Machine Learning Engineering

What you'll learn

Understanding the fundamentals of Kubernetes: Participants will gain knowledge about the basic concepts, architecture, and components of Kubernetes.

Creating and managing Kubernetes clusters: Participants will learn how to install and configure Kubernetes clusters.

Deploying applications in a Kubernetes environment: Participants will learn how to prepare applications for deployment in a cluster.

Managing and scaling applications in Kubernetes: Participants will understand how to effectively manage and scale applications running in Kubernetes.

Requirements

Computer

Description

The Kubernetes Quest: Next-Level ML Engineering course is designed to empower machine learning engineers with the skills and knowledge to leverage Kubernetes for advanced ML engineering workflows. In this course, participants will dive deep into the integration of Kubernetes with machine learning pipelines, enabling them to efficiently manage and scale ML workloads in production environments.Through a combination of theoretical lectures, hands-on exercises, and real-world use cases, participants will gain practical expertise in leveraging Kubernetes to orchestrate and deploy ML models at scale. They will learn how to effectively manage computational resources, automate deployment and scaling, and ensure high availability and fault tolerance for their ML applications.Participants will explore advanced topics such as Kubernetes networking for ML applications, optimizing resource utilization with Kubernetes schedulers, implementing secure authentication and authorization mechanisms, and integrating ML-specific tools and frameworks within Kubernetes ecosystems. By the end of the course, participants will be equipped with comprehensive knowledge and skills to confidently navigate the intersection of Kubernetes and ML engineering, empowering them to deliver robust and scalable ML solutions in complex production environments.Moreover, participants will learn best practices for monitoring ML workloads, troubleshooting common issues, and implementing advanced Kubernetes features like custom resource definitions (CRDs) and operators for ML-specific use cases.

Overview

Section 1: Wstęp

Lecture 1 Introduction

Section 2: CI / CD Theory

Lecture 2 CI/CD I

Lecture 3 CI/CD II

Lecture 4 ML CI/CD

Section 3: MLOps Real world scenarios

Lecture 5 Netflix

Lecture 6 Uber

Lecture 7 Google

Lecture 8 Airbnb

Section 4: Network Key Concepts

Lecture 9 Storage, Security, Networking

Lecture 10 Internet Traffic

Lecture 11 Network Policy

Lecture 12 Virtual Machine

Lecture 13 Load Balancing

Lecture 14 Auto Scalling

Lecture 15 Service Mesh

Lecture 16 Blue/Green Deployments

Section 5: Kubernetes Key Concepts

Lecture 17 Clusters

Lecture 18 Nodes

Lecture 19 Pods

Lecture 20 Helm

Lecture 21 Ansible

Lecture 22 Prometheus

Lecture 23 Semaphore

Section 6: Kubernetes Kind Services

Lecture 24 NodePort

Lecture 25 ClusterIp

Lecture 26 Deployment

Section 7: Practise

Lecture 27 Create Resources

Lecture 28 Describe Resources

Lecture 29 Deploy React App

Lecture 30 Connect to AKS

Lecture 31 Cost Analysis

Section 8: AKS Pipeline

Lecture 32 Introduction

Lecture 33 Secrets

Lecture 34 OIDC Auth

Lecture 35 Container Registry

Lecture 36 Push to ACR

Lecture 37 Create AKS

Lecture 38 Pod Deployed

Section 9: TensorFlow Kubernetes

Lecture 39 TensorFlowDocker

Lecture 40 TrainMLWithDocker

Lecture 41 KubernetesManifest

Lecture 42 KubernetesFromDockerHub

Lecture 43 MLModelInPod

Section 10: ML artifact in pipeline

Lecture 44 Test

Lecture 45 Verify

Lecture 46 Integrate

Section 11: Summary

Lecture 47 Summary

Engineers,Programmers