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Learner Reviews & Feedback for MLOps Platforms: Amazon SageMaker and Azure ML by Duke University

3.7
stars
38 ratings

About the Course

In MLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure ML you will learn the necessary skills to build, train, and deploy machine learning solutions in a production environment using two leading cloud platforms: Amazon Web Services (AWS) and Microsoft Azure. This course is also a great resource for individuals looking to prepare for AWS or Azure machine learning certifications or who are working (or seek to work) as data scientists, software engineers, software developers, data analysts, or other roles that use machine learning. Through a series of hands-on exercises, you will gain an intuition for basic machine learning algorithms and practical experience working with these leading Cloud platforms. By the end of the course, you will be able to deploy machine learning solutions in a production environment using AWS and Azure technology. Week 1. Explore data engineering with AWS technology. We’ll discuss topics such as getting started with machine learning on AWS, creating data repositories, and identifying and implementing solutions for data ingestion and transformation. Week 2. Gain basic data science skills with AWS technology. You will learn data cleaning techniques, perform feature engineering, data analysis, and data visualization for machine learning. We’ll prioritize using serverless solutions that are available on AWS to make the process more efficient. Week 3. Learn machine learning models with AWS technology. We’ll examine how to select appropriate models for the task at hand, choose hyperparameters, train models on the platform, and evaluate models. Week 4. Learn MLOps with AWS: the final phase of putting machine learning into production. We’ll discuss topics such as operationalizing a machine learning model, deciding between CPU and GPU, and deploying and maintaining the model. Week 5. Learn how to work with data and machine learning in a second leading Cloud-based platform: Azure ML....

Top reviews

ND

Aug 21, 2024

Great learning resources that will be useful long after completing the course, concise presentations, and clear explanations of all topics

ZZ

Apr 30, 2023

The best course so far I have taken, I am looking forward to enchace my skills more in MLOps, I have to do few projects

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1 - 6 of 6 Reviews for MLOps Platforms: Amazon SageMaker and Azure ML

By Georgi K

•

Nov 11, 2023

I am subscribed to the entire specialization, so this is my third course and review and I have to say it does not disappoint. Similar to the previous 2 courses the video order from the very beginning is messed up. We are shown the most basic of operations – how to start an AWS cloud shell and split your screen but more complex things are brushed off with just a link to the AWS documentation. The course still has no structure, honestly, I do not know what the whole point is, what are we working towards. The quizzes got even worse, and the number of questions was reduced from 10 to 5 to 1 with some absurd questions like what is EDA, why perform feature engineering. Course 3 of 4 of an Advanced Level specialization keeps explaining structured vs unstructured machine learning; train, test splits etc beginner topics. I do not recommend taking this course or the entire specialization even if you get them for free.

By Mariusz M

•

Jul 17, 2024

First 4 modules - just a collection of random talks, just one minute devoted to Sagemaker. MLOps is just a phrase in the course title, the content is just some bits and pieces which may, by chance, somehow come handy if you learn MLOps elsewhere.

By Nicole D

•

Aug 22, 2024

Great learning resources that will be useful long after completing the course, concise presentations, and clear explanations of all topics

By Zaffer

•

Apr 30, 2023

The best course so far I have taken, I am looking forward to enchace my skills more in MLOps, I have to do few projects

By Enrique A

•

Oct 16, 2023

excellent

By PASUMARTY S V S

•

Dec 12, 2023

Awesome!