MLOps Fundamentals – Learn MLOps Concepts with Azure demo

MLOps Fundamentals – Learn MLOps Concepts with Azure demo, available at $69.99, has an average rating of 4.52, with 35 lectures, 1 quizzes, based on 6057 reviews, and has 19075 subscribers.

You will learn about Basics of MLOps, benefits and its implementation. Challenges faced by teams in the current way of handling Machine learning projects. Importance of MLOps principles in Machine learning projects. Standards and principles followed in MLOps culture. What is continuous integration, continuous delivery and continuous training in MLOps space. Various maturity levels associated with MLOps. MLOps tools stack and various MLOps platforms comparison. Run an end-to-end CI/CD MLOps pipeline using Azure DevOps & Azure Machine learning. This course is ideal for individuals who are Data scientists or Data engineers or ML engineers or Devops engineers It is particularly useful for Data scientists or Data engineers or ML engineers or Devops engineers.

Enroll now: MLOps Fundamentals – Learn MLOps Concepts with Azure demo

Summary

Title: MLOps Fundamentals – Learn MLOps Concepts with Azure demo

Price: $69.99

Average Rating: 4.52

Number of Lectures: 35

Number of Quizzes: 1

Number of Published Lectures: 35

Number of Published Quizzes: 1

Number of Curriculum Items: 36

Number of Published Curriculum Objects: 36

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of MLOps, benefits and its implementation.
Challenges faced by teams in the current way of handling Machine learning projects.
Importance of MLOps principles in Machine learning projects.
Standards and principles followed in MLOps culture.
What is continuous integration, continuous delivery and continuous training in MLOps space.
Various maturity levels associated with MLOps.
MLOps tools stack and various MLOps platforms comparison.
Run an end-to-end CI/CD MLOps pipeline using Azure DevOps & Azure Machine learning.

Who Should Attend

Data scientists
Data engineers
ML engineers
Devops engineers

Target Audiences

Data scientists
Data engineers
ML engineers
Devops engineers

Important Note: The intention of this course is to teach MLOps fundamentals and not pure Azure ML. Azure demo section is included to show the working of an end-to-end MLOps project. All the codes involved in Azure MLOps pipeline are well explained though.

“MLOps is a culture with set of principles, guidelines defined in machine learning world for smooth implementation and productionization of Machine learning models.”

Data scientists have been experimenting with Machine learning models from long time, but to provide the real business value, they must be deployed to production. Unfortunately, due to the current challenges and non-systemization in ML lifecycle, 80% of the models never make it to production and remain stagnated as an academic experiment only.

Machine Learning Operations (MLOps), emerged as a solution to the problem, is a new culture in the market and a rapidly growing space that encompasses everything required to deploy a machine learning model into production.

As per the tech talks in market, 2024 is the year of MLOps and would become the mandate skill set for Enterprise Machine Learning projects.

What’s included in the course ?

MLOps core basics and fundamentals.
What were the challenges in the traditional machine learning lifecycle management.
How MLOps is addressing those issues while providing more flexibility and automation in the ML process.
Standards and principles on which MLOps is based upon.
Continuous integration (CI), Continuous delivery (CD) and Continuous training (CT) pipelines in MLOps.
Various maturity levels associated with MLOps.
MLOps tools stack and MLOps platforms comparisons.
Quick crash course on Azure Machine learning components.
An end-to-end CI/CD MLOps pipeline for a case study in Azure using Azure DevOps & Azure Machine learning.

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is MLOps

Lecture 2: Traditional Machine Learning Lifecycle – Part 1

Lecture 3: Traditional Machine Learning Lifecycle – Part 2

Lecture 4: Roles & Responsibilities in ML projects

Chapter 2: Challenges in existing ML projects

Lecture 1: Problems in traditional ML lifecycle

Lecture 2: Activities needed to productionize models

Chapter 3: MLOps – A solution

Lecture 1: Standards/Principles in MLOps

Lecture 2: MLOps implementation

Lecture 3: Benefits of MLOps

Lecture 4: Difference between DevOps & MLOps

Chapter 4: Maturity levels in MLOps

Lecture 1: MLOps level 0

Lecture 2: MLOps level 1

Lecture 3: MLOps level 2

Lecture 4: Importance of Maturity levels

Chapter 5: MLOps Tools/Platforms Stack

Lecture 1: MLOps Platform requirements

Lecture 2: MLOps Platforms comparison

Lecture 3: Which MLOps Platform to choose?

Chapter 6: Demo – Project Requirements

Lecture 1: Note

Lecture 2: Project requirements

Chapter 7: Azure Machine Learning Studio – Crash course

Lecture 1: Introduction to Azure Machine Learning

Lecture 2: Azure Machine learning studio UI Tour

Chapter 8: Demo – Data scientist's experiment

Lecture 1: EDA notebook

Lecture 2: Azure DevOps & Azure ML connections

Lecture 3: Training & Evaluation notebook

Chapter 9: Demo – Orchestrated ML codes in Azure

Lecture 1: Model Training code

Lecture 2: Model Evaluation code

Lecture 3: Model Registry code

Lecture 4: Scoring code

Chapter 10: Demo – CI/CD MLOps Pipeline in Azure

Lecture 1: Overview

Lecture 2: Continuous Integration (CI) script

Lecture 3: Code to publish the pipeline

Lecture 4: Code to run the published package

Lecture 5: Continuous Deployment (CD) script

Lecture 6: Run the Pipeline

Chapter 11: Bonus

Lecture 1: Bonus

Instructors

J Garg – Real Time Learning
Data Engineering, Analytics and Cloud Trainer

Rating Distribution

1 stars: 45 votes
2 stars: 119 votes
3 stars: 703 votes
4 stars: 2369 votes
5 stars: 2821 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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