We may earn an affiliate commission when you visit our partners.
Noah Gift
- Discover the principles of machine learning engineering and its role in building scalable, intelligent systems.
- Learn to develop machine learning applications using software engineering best practices and continuous delivery pipelines.
- Explore AutoML technologies for efficient model training with minimal coding effort.
- Gain hands-on experience with open-source and cloud-based AutoML solutions like Ludwig and Cloud AutoML.
- Dive into emerging topics such as MLOps, edge machine learning, and AI APIs for cutting-edge applications.
Register for this course and see more details by visiting:
OpenCourser.com/course/zg1o0u/cloud
What's inside
Learning objectives
- Evaluate machine learning engineering best practices
- Build machine learning applications
- Utilize continuous delivery for machine learning
- Summarize automl concepts and strategies
- Evaluate open-source automl with ludwig
- Utilize cloud-based automl solutions
- Simplify mlops strategies
- Interpret emerging topics in edge machine learning
- Develop solutions using ai apis
- Evaluate machine learning engineering best practices
- Build machine learning applications
- Utilize continuous delivery for machine learning
- Summarize automl concepts and strategies
- Evaluate open-source automl with ludwig
- Utilize cloud-based automl solutions
- Simplify mlops strategies
- Interpret emerging topics in edge machine learning
- Develop solutions using ai apis
Syllabus
Getting Started with Machine Learning Engineering
Module 1 (3 hours)
Videos:
Instructor Introduction (1 minute)
Read more
Syllabus
Getting Started with Machine Learning Engineering
Module 1 (3 hours)
Videos:
Instructor Introduction (1 minute)
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches best practices in machine learning engineering, which is crucial for professionals in the field
Develops expertise in continuous delivery pipelines, empowering learners to build scalable ML systems
Provides hands-on experience with industry-standard AutoML solutions, enhancing learners' abilities to efficiently train models
Explores cutting-edge topics such as MLOps and edge machine learning, preparing learners for the future of ML engineering
Covers diverse aspects of ML engineering, making it suitable for learners with various backgrounds
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Practical mlops and cloud ml engineering
According to students, this course offers a largely positive overview of Cloud ML Engineering and MLOps. Learners highly value its practical approach and hands-on labs, spanning AWS, GCP, and Azure, deeming it highly relevant for operationalizing ML models. It covers modern MLOps practices, AutoML concepts like Ludwig, and various AI APIs, providing a comprehensive landscape understanding. While some noted a lack of depth in certain areas or short videos, the course effectively bridges theoretical ML with production readiness. Recent feedback emphasizes its continued relevance and current topics like Edge ML.
Instructor provides clear explanations for complex concepts.
"Instructor explains complex topics clearly, making them easy to grasp."
"The instructor's expertise shines through, providing confidence in the content."
"The instructor's pacing was excellent, allowing me to follow along comfortably."
Provides valuable insights across major cloud platforms.
"I particularly liked the focus on cloud-based solutions across AWS, GCP, and Azure."
"The labs covered different cloud providers, which was a significant plus for understanding the broader ecosystem."
"It connects a lot of dots across various cloud AutoML services, offering a well-rounded view."
Addresses highly current and relevant topics like MLOps and Edge ML.
"The topics on AI APIs and Edge ML were very current and relevant, making the course highly valuable."
"The focus on modern MLOps practices is spot on and reflects current industry needs."
"This course provided a strong foundation in MLOps, practical deployment strategies, and continuous delivery, which are essential today."
Offers valuable practical experience for ML operationalization.
"The labs are incredibly practical, especially the continuous deployment parts. I particularly liked the focus on cloud-based solutions."
"I found the practical examples with Flask and various cloud AI APIs very useful."
"The hands-on coding and projects are the strongest part of the course for me, really cementing the concepts."
"I particularly enjoyed the hands-on labs that covered different cloud providers."
Some videos are very concise, requiring additional self-study.
"Some of the videos are very short, almost like introductions without much depth."
"The 'videos' often feel like glorified bullet points rather than detailed explanations. I expected more depth."
"I wish there were more extensive video content for real ML engineering challenges."
Covers many MLOps topics, sometimes lacking deep dives.
"The course touches on many important topics but sometimes feels a bit superficial, especially the depth of some cloud services."
"Disappointed with the lack of depth. Many topics are just briefly introduced."
"It's a good starting point, but you'll need external resources for deeper understanding, especially with MLOps."
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 Cloud Machine Learning Engineering and MLOps with these
activities:
Review and Organize Course Notes
Show steps
Regularly reviewing and organizing your course notes will help you retain the information better and improve your ability to recall it during assessments.
Browse courses on
Note-Taking
Show steps
-
After each lecture or module, set aside time to review your notes and identify key concepts.
-
Organize your notes into a logical structure, such as by topic or chronological order.
-
Highlight or color-code important sections to make them easier to find.
-
Use a note-taking app or digital notebook for ease of organization and accessibility.
Form a Study Group with Classmates
Show steps
Forming a study group with classmates will provide you with a supportive environment to discuss concepts, share resources, and work through problems together.
Browse courses on
Peer Support
Show steps
-
Reach out to classmates via email or online forums to gauge interest.
-
Set up regular meeting times and a communication platform (e.g., Zoom, Slack).
-
Establish clear roles and expectations for each member.
-
Meet consistently and focus on actively engaging with the course material.
Review Command Line Basics
Show steps
Reviewing the basics of the command line will prepare you for tasks where you need to navigate the command line in future modules.
Show steps
-
Read a tutorial on basic command line commands.
-
Open a terminal window or command prompt.
-
Practice running basic commands like 'ls' and 'cd' to navigate the file system.
-
Experiment with pipes and redirection using commands like 'grep' and '>'
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a Simple Flask API Using a Tutorial
Show steps
Following a tutorial to create a simple Flask API will give you hands-on experience with a key framework used in building scalable machine learning applications.
Browse courses on
RESTful APIs
Show steps
-
Find a beginner-friendly tutorial on creating a Flask API.
-
Set up a development environment with Python and Flask installed.
-
Follow the tutorial step-by-step to create a basic API with endpoints and data handling.
-
Test the API using a tool like Postman or curl.
Solve LeetCode Problems on Machine Learning Algorithms
Show steps
Solving LeetCode problems on machine learning algorithms will help you practice implementing and applying machine learning concepts in a practical setting.
Browse courses on
Problem Solving
Show steps
-
Sign up for a LeetCode account.
-
Choose problems related to machine learning algorithms like classification or regression.
-
Solve the problems in your preferred programming language.
-
Review solutions and common approaches to gain insights and improve your problem-solving skills.
Create a Glossary of Key Machine Learning Concepts
Show steps
Creating a glossary of key machine learning concepts will help you solidify your understanding of the terminology and technical vocabulary used in the field.
Browse courses on
Glossary Creation
Show steps
-
Review the course readings and materials to identify important concepts.
-
Define each concept clearly and concisely.
-
Organize the glossary alphabetically or by category.
-
Review and expand the glossary regularly.
Contribute to an Open-Source AutoML Project
Show steps
Contributing to an open-source AutoML project will give you practical experience working with AutoML tools and contribute to the development of the field.
Browse courses on
AutoML
Show steps
-
Identify an open-source AutoML project on platforms like GitHub or GitLab.
-
Review the project's documentation and contribution guidelines.
-
Choose an area where you can make a meaningful contribution, such as bug fixes, feature enhancements, or documentation improvements.
-
Submit a pull request with your changes and follow the project's review process.
Review and Organize Course Notes
Show steps
Regularly reviewing and organizing your course notes will help you retain the information better and improve your ability to recall it during assessments.
Browse courses on
Note-Taking
Show steps
Show steps
Show steps
- After each lecture or module, set aside time to review your notes and identify key concepts.
- Organize your notes into a logical structure, such as by topic or chronological order.
- Highlight or color-code important sections to make them easier to find.
- Use a note-taking app or digital notebook for ease of organization and accessibility.
Form a Study Group with Classmates
Show steps
Forming a study group with classmates will provide you with a supportive environment to discuss concepts, share resources, and work through problems together.
Browse courses on
Peer Support
Show steps
Show steps
Show steps
- Reach out to classmates via email or online forums to gauge interest.
- Set up regular meeting times and a communication platform (e.g., Zoom, Slack).
- Establish clear roles and expectations for each member.
- Meet consistently and focus on actively engaging with the course material.
Review Command Line Basics
Show steps
Reviewing the basics of the command line will prepare you for tasks where you need to navigate the command line in future modules.
Show steps
Show steps
Show steps
- Read a tutorial on basic command line commands.
- Open a terminal window or command prompt.
- Practice running basic commands like 'ls' and 'cd' to navigate the file system.
- Experiment with pipes and redirection using commands like 'grep' and '>'
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a Simple Flask API Using a Tutorial
Show steps
Following a tutorial to create a simple Flask API will give you hands-on experience with a key framework used in building scalable machine learning applications.
Browse courses on
RESTful APIs
Show steps
Show steps
Show steps
- Find a beginner-friendly tutorial on creating a Flask API.
- Set up a development environment with Python and Flask installed.
- Follow the tutorial step-by-step to create a basic API with endpoints and data handling.
- Test the API using a tool like Postman or curl.
Solve LeetCode Problems on Machine Learning Algorithms
Show steps
Solving LeetCode problems on machine learning algorithms will help you practice implementing and applying machine learning concepts in a practical setting.
Browse courses on
Problem Solving
Show steps
Show steps
Show steps
- Sign up for a LeetCode account.
- Choose problems related to machine learning algorithms like classification or regression.
- Solve the problems in your preferred programming language.
- Review solutions and common approaches to gain insights and improve your problem-solving skills.
Create a Glossary of Key Machine Learning Concepts
Show steps
Creating a glossary of key machine learning concepts will help you solidify your understanding of the terminology and technical vocabulary used in the field.
Browse courses on
Glossary Creation
Show steps
Show steps
Show steps
- Review the course readings and materials to identify important concepts.
- Define each concept clearly and concisely.
- Organize the glossary alphabetically or by category.
- Review and expand the glossary regularly.
Contribute to an Open-Source AutoML Project
Show steps
Contributing to an open-source AutoML project will give you practical experience working with AutoML tools and contribute to the development of the field.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Identify an open-source AutoML project on platforms like GitHub or GitLab.
- Review the project's documentation and contribution guidelines.
- Choose an area where you can make a meaningful contribution, such as bug fixes, feature enhancements, or documentation improvements.
- Submit a pull request with your changes and follow the project's review process.
Career center
Learners who complete Cloud Machine Learning Engineering and MLOps will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/zg1o0u/cloud
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zg1o0u/cloud
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructor
Noah Gift
Language
English
Transcript
English
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser