MLOps Platforms: Amazon SageMaker and Azure ML from edX

Master Cloud MLOps: AWS SageMaker & Azure ML

Build end-to-end machine learning pipelines on leading cloud platforms
Gain practical experience through hands-on exercises and projects
Prepare for AWS & Azure ML certifications and job roles

Course Highlights:

Explore data engineering & ML foundations on AWS
Create data repos, ETL pipelines & serverless solutions
Learn data science skills - cleaning, visualization, analysis
Train, select & tune ML models on AWS SageMaker
Operationalize models for production with MLOps best practices
Deploy & maintain ML solutions using CPU/GPU instances

Ideal for data scientists, ML engineers, analysts & cloud professionals. Master comprehensive MLOps skills on AWS & Azure through real-world training.

What's inside

Learning objectives

Apply exploratory data analysis (eda) techniques to data science problems and datasets.
Build machine learning modeling solutions using both aws and azure technology.

Train and deploy machine learning solutions to a production environment using cloud technology.

Apply exploratory data analysis (eda) techniques to data science problems and datasets.
Build machine learning modeling solutions using both aws and azure technology.
Train and deploy machine learning solutions to a production environment using cloud technology.

Syllabus

Module 1: Data Engineering with AWS Technology (7 hours)

\- Video: Meet your Course Instructor: Noah Gift (3 minutes)

\- Video: Using Sagemaker Studio Lab (7 minutes)

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops machine learning pipelines, which are standard in industry

Builds a strong foundation in Cloud MLOps, which is highly relevant to cloud computing

Taught by Alfredo Deza and Noah Gift, who are recognized for their work in Cloud MLOps

Examines MLOps on AWS and Azure, which are highly relevant to cloud computing

Teaches skills and tools that can strengthen an existing foundation for intermediate learners

Teaches machine learning modeling, which is a core skill for data scientists and ML engineers

Reviews summary

Practical mlops on aws and azure

According to students, this course offers a highly practical and hands-on approach to MLOps, with a strong emphasis on AWS SageMaker for data engineering and model deployment. Learners particularly praise the clear explanations from the instructor and the real-world applicability of the concepts. Many found it valuable for career development and certification preparation. However, a recurring point for some is that the Azure ML content is less comprehensive and feels rushed compared to the AWS sections. Additionally, several reviews, including recent ones, mention challenges with labs being outdated or requiring troubleshooting, suggesting a need for updates to maintain the seamless hands-on experience.

Instructor delivers complex topics with clarity and good pace.

"The instructor, Noah Gift, explains complex topics clearly. Highly recommend for anyone serious about MLOps."

"The instructor's pace is just right, and the explanations are spot on."

"I appreciated how the instructor simplified advanced MLOps topics, making them easy to grasp."

Provides comprehensive coverage of MLOps on AWS SageMaker.

"The AWS modules are comprehensive, covering S3, Step Functions, and SageMaker in good detail."

"It's heavily skewed towards AWS, which is fine as that's what I primarily use, but those looking for equal Azure coverage might be disappointed."

"The coverage of data engineering and model deployment on AWS SageMaker was excellent. A must-take for MLOps aspiring professionals."

Excellent for hands-on application of MLOps concepts.

"This course is incredibly practical and hands-on. The labs for SageMaker were particularly useful, helping me understand MLOps concepts end-to-end."

"The blend of theoretical concepts and practical application on SageMaker is perfect. One of the best MLOps courses I've taken."

"I was able to apply what I learned immediately in my job. The detailed walk-throughs in the labs were immensely helpful."

Pacing can be fast, may require prior knowledge or extra effort.

"I found the course to be too fast-paced and some explanations felt superficial, especially in the later modules."

"It's a good overview if you're a complete beginner, but intermediate users might find it lacking in advanced topics."

"Unless you are prepared for significant self-study, this course might be challenging for absolute beginners."

Some labs require troubleshooting due to being outdated.

"I found some of the AWS labs to be slightly outdated or not working out-of-the-box, requiring quite a bit of troubleshooting."

"The labs frequently had issues, and it took a lot of external research to get them working. Not recommended for absolute beginners..."

"The course has valuable content, but the lab environments can be frustrating. I spent more time debugging than learning."

Azure ML section is brief, lacking depth compared to AWS.

"I found the Azure ML part a bit rushed and less deep compared to AWS. Still, it provides a good starting point for exploring both platforms."

"The Azure section is very brief and doesn't offer the same depth as the AWS part. It provides a good overview if you're a complete beginner..."

"The Azure part was almost an afterthought. The Azure module is very light. If you're mainly interested in AWS, this course delivers."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in MLOps Platforms: Amazon SageMaker and Azure ML with these activities:

Brush up on Azure ML workflow

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This activity will help you refresh your knowledge of concepts relevant to Azure ML, giving you a better foundation before the course begins.

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Look at your notebook from previous coursework in Azure ML.
Go through Azure's beginner documentation for ML.
Look at content by Microsoft Learning for Azure ML.

Review Machine Learning modeling concepts

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This activity will help you refresh your knowledge of concepts relevant to Machine Learning models, giving you a better foundation before the course begins.

Browse courses on Machine Learning

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Look over class notes and assignments from your previous Machine Learning course.
Go through a quick online tutorial about Machine Learning models.

Compile tools and documentation

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This activity will assist you in organizing course materials, helping you be more efficient in the long run.

Browse courses on Tools

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Set up a digital folder where you will keep resources.
Bookmark useful documentation.
Create a list of tools you learn about in class.

Five other activities

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Practice gradient descent

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This activity will help you practice key skills and concepts necessary for the topic of gradient descent.

Browse courses on Gradient Descent

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Look through the provided interactive gradient descent sandbox.

Learn from AWS Academy

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This activity will help you expand your learning and build a stronger understanding of Machine Learning.

Browse courses on AWS

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Enroll in AWS Academy Machine Learning Foundations.
Complete the onboarding and course structure materials.
Go through the first two modules in your own time.

Read Pattern Recognition and Machine Learning

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This book will provide a comprehensive overview of machine learning and help you reinforce the fundamental principles covered in the course.

View Pattern Recognition and Machine Learning... on Amazon

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Read the first three chapters.
Do the exercises from the first three chapters.
Go to the course forum and discuss the book with other students.

Review CI/CD tools

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This activity will help you understand a topic of interest for expanding on what is taught in the course.

Browse courses on Continuous Integration

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Look at beginner resources for DevOps.
Go through tutorials for AWS CodeWhisperer AI Pair Programming Tool.

Deploy a machine learning model

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This project will give you hands-on experience deploying a model, reinforcing the skills and knowledge you have learned through the course.

Browse courses on Machine Learning

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Choose a dataset.
Build a model.
Train the model.
Deploy the model.

Career center

Learners who complete MLOps Platforms: Amazon SageMaker and Azure ML will develop knowledge and skills that may be useful to these careers:

Reading list

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MLOps Platforms

Amazon SageMaker and Azure ML

What's inside

Learning objectives

Syllabus

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Reviews summary

Practical mlops on aws and azure

Activities

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