Kubernetes Quest Next-Level ML Engineering

Kubernetes Quest Next-Level ML Engineering, available at $34.99, has an average rating of 4.5, with 46 lectures, based on 1 reviews, and has 42 subscribers.

You will learn about 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. This course is ideal for individuals who are Engineers or Programmers It is particularly useful for Engineers or Programmers.

Enroll now: Kubernetes Quest Next-Level ML Engineering

Summary

Title: Kubernetes Quest Next-Level ML Engineering

Price: $34.99

Average Rating: 4.5

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Engineers

Programmers

Target Audiences

Engineers

Programmers

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.

Course Curriculum

Chapter 1: Wst?p

Lecture 1: Introduction

Chapter 2: CI / CD Theory

Lecture 1: CI/CD I

Lecture 2: CI/CD II

Lecture 3: ML CI/CD

Chapter 3: MLOps Real world scenarios

Lecture 1: Netflix

Lecture 2: Uber

Lecture 3: Google

Lecture 4: Airbnb

Chapter 4: Network Key Concepts

Lecture 1: Storage, Security, Networking

Lecture 2: Internet Traffic

Lecture 3: Network Policy

Lecture 4: Virtual Machine

Lecture 5: Load Balancing

Lecture 6: Auto Scalling

Lecture 7: Service Mesh

Lecture 8: Blue/Green Deployments

Chapter 5: Kubernetes Key Concepts

Lecture 1: Clusters

Lecture 2: Nodes

Lecture 3: Pods

Lecture 4: Helm

Lecture 5: Ansible

Lecture 6: Prometheus

Lecture 7: Semaphore

Chapter 6: Kubernetes Kind Services

Lecture 1: NodePort

Lecture 2: ClusterIp

Lecture 3: Deployment

Chapter 7: Practise

Lecture 1: Create Resources

Lecture 2: Describe Resources

Lecture 3: Connect to AKS

Lecture 4: Cost Analysis

Chapter 8: AKS Pipeline

Lecture 1: Introduction

Lecture 2: Secrets

Lecture 3: OIDC Auth

Lecture 4: Container Registry

Lecture 5: Push to ACR

Lecture 6: Create AKS

Lecture 7: Pod Deployed

Chapter 9: TensorFlow Kubernetes

Lecture 1: TensorFlowDocker

Lecture 2: TrainMLWithDocker

Lecture 3: KubernetesManifest

Lecture 4: KubernetesFromDockerHub

Lecture 5: MLModelInPod

Chapter 10: ML artifact in pipeline

Lecture 1: Test

Lecture 2: Verify

Lecture 3: Integrate

Chapter 11: Summary

Lecture 1: Summary

Instructors

Piotr ?ak
Software Engineer

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4 stars: 1 votes

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