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.
MLOps Platforms: Amazon SageMaker and Azure ML
MLOps Platforms: Amazon SageMaker and Azure ML
This course is part of MLOps | Machine Learning Operations Specialization
Instructors: Noah Gift
6,462 already enrolled
Included with
Gain insight into a topic and learn the fundamentals.
3.7
(38 reviews)
Advanced level
Recommended experience
30 hours to complete
3 weeks at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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.
Skills you'll gain
Details to know
Earn a career certificate
Add to your LinkedIn profile
Assessments
17 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the MLOps | Machine Learning Operations Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
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There are 5 modules in this course
In this module, you will learn how to build data engineering solutions on AWS and apply it by building a data engineering pipeline with AWS Step Functions and AWS Lambda.
What's included
16 videos15 readings4 assignments2 discussion prompts1 ungraded lab
In this module, you will compose data engineering solutions using AWS technology and apply it by building data science notebooks.
What's included
7 videos9 readings3 assignments4 ungraded labs
In this module, you will compose machine learning modeling solutions using AWS technology and apply it by building a linear regression model that runs inside a command-line tool.
What's included
12 videos11 readings4 assignments3 ungraded labs
In this module, you will learn to deploy and operationalize machine learning solutions using AWS technology and apply it by fine-tuning a Hugging face model using Sagemaker Studio Lab.
What's included
14 videos12 readings3 assignments1 ungraded lab
In this module, you will learn about Machine Learning certifications from the major cloud providers and how to apply them to MLOps. You will learn about services related to Machine Learning and ML Engineering tasks like AutoML and how they apply to the certifications.
What's included
15 videos7 readings3 assignments
Instructors
Instructor ratings
3.8 (13 ratings)Offered by
Recommended if you're interested in Machine Learning
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Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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