MLOps Simplified



It’s not a course, it’s all the best courses in one

What you will learn

Understand the fundamental concepts of MLOps and its importance in the machine learning lifecycle

Learn how to deploy machine learning models in production using various MLOps tools and frameworks

Learn how to monitor and manage machine learning models in production

Understand the role of DevOps in MLOps and how to integrate the two practices

Learn how to implement best practices for MLOps, including version control, testing, and documentation

Description

Our courses bring together the best resources from leading universities, companies, entrepreneurs and academics around the world to deliver a truly unparalleled learning experience.

Don’t waste your money, our team of expert curators offers carefully curated education, providing the highest quality educational resources from the most respected institutions and industry leaders to create the ultimate MLOps Simplified course, an opportunity to acquire the best knowledge and skills in the field, providing the most efficient and effective types of objects.

THIS IS A EBOOK COURSE, A COMPILATION OF THE BEST EDUCATIONAL RESOURCES OF THE WORLD.

IT INCLUDES TEXTS, CODING EXAMPLES, CASE STUDIES AND OPTIONAL EVALUATIONS.

Course Description:

MLOps, or Machine Learning Operations, is the practice of combining machine learning and operations to improve the speed and quality of deploying machine learning models in production. This course covers the latest techniques and tools used in MLOps, including model deployment, monitoring, and management.

Course Objectives:

Understand the fundamental concepts of MLOps and its importance in the machine learning lifecycle


  • Learn how to deploy machine learning models in production using various MLOps tools and frameworks
  • Learn how to monitor and manage machine learning models in production
  • Understand the role of DevOps in MLOps and how to integrate the two practices
  • Learn how to implement best practices for MLOps, including version control, testing, and documentation

Course Outline:

Week 1: Introduction to MLOps

  • Introduction to MLOps and its importance in the machine learning lifecycle
  • Overview of the machine learning lifecycle and the role of MLOps in each stage

Week 2: Model Deployment

  • Introduction to model deployment
  • Techniques for deploying machine learning models in production
  • Hands-on deployment using various MLOps tools and frameworks

Week 3: Model Monitoring and Management

  • Introduction to model monitoring and management
  • Techniques for monitoring and managing machine learning models in production
  • Hands-on monitoring and management using various MLOps tools and frameworks

Week 4: DevOps and MLOps Integration

  • Introduction to DevOps and its importance in MLOps
  • Techniques for integrating DevOps and MLOps practices
  • Hands-on integration using various MLOps tools and frameworks

Week 5: MLOps Best Practices

  • Introduction to best practices for MLOps
  • Implementing version control, testing, and documentation in MLOps
  • Hands-on implementation using various MLOps tools and frameworks

Week 6: Capstone Project

  • Students will work on a capstone project to apply the skills and knowledge learned in the course
  • Students will present their projects to the class
English
language

Content

Welcome

Syllabus

Introduction to MLOps

Introduction to MLOps and its importance in the machine learning lifecycle
Overview of the machine learning lifecycle and the role of MLOps in each stage
MLOps Overview
AIOps vs. MLOps: What’s the Difference?
Running a Kubeflow pipeline in less than 3 minutes

Model Deployment

Introduction to model deployment
Techniques for deploying machine learning models in production
Hands-on deployment using various MLOps tools and frameworks

Model Monitoring and Management

Introduction to model monitoring and management
Techniques for monitoring and managing machine learning models in production
Hands-on monitoring and management using various MLOps tools and frameworks

DevOps and MLOps Integration

Introduction to DevOps and its importance in MLOps
Techniques for integrating DevOps and MLOps practices
Hands-on integration using various MLOps tools and frameworks

MLOps Best Practices

Introduction to best practices for MLOps
Implementing version control, testing, and documentation in MLOps
Hands-on implementation using various MLOps tools and frameworks

Capstone Project

Applying the skills and knowledge learned in the course
Extra material
MLOps: specialized DevOps for Machine Learning – Stefan Nica

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