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Noah Gift
Welcome to the fourth course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will build upon the Cloud computing and data engineering concepts introduced in the first three courses to apply Machine Learning Engineering to real-world projects. First, you will develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications. Then, you will learn to use AutoML to solve problems more efficiently than traditional machine learning approaches alone. Finally, you will dive into emerging topics in Machine Learning including MLOps, Edge Machine Learning and AI APIs.
Read more
Welcome to the fourth course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will build upon the Cloud computing and data engineering concepts introduced in the first three courses to apply Machine Learning Engineering to real-world projects. First, you will develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications. Then, you will learn to use AutoML to solve problems more efficiently than traditional machine learning approaches alone. Finally, you will dive into emerging topics in Machine Learning including MLOps, Edge Machine Learning and AI APIs.
Read more
Welcome to the fourth course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will build upon the Cloud computing and data engineering concepts introduced in the first three courses to apply Machine Learning Engineering to real-world projects. First, you will develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications. Then, you will learn to use AutoML to solve problems more efficiently than traditional machine learning approaches alone. Finally, you will dive into emerging topics in Machine Learning including MLOps, Edge Machine Learning and AI APIs.
This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a Flask web application that serves out Machine Learning predictions.
Register for this course and see more details by visiting:
OpenCourser.com/course/47wowm/cloud
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What's inside
Syllabus
Getting Started with Machine Learning Engineering
This week, you will learn about the methodologies involved in Machine Learning Engineering. By the end of the week, you will be able to develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications.
Read more
Syllabus
Getting Started with Machine Learning Engineering
This week, you will learn about the methodologies involved in Machine Learning Engineering. By the end of the week, you will be able to develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications.
Read more
Using AutoML
This week, you will learn about AutoML and how to use it to build efficient Machine Learning solutions with little to no code. These technologies include Ludwig, Google AutoML, Apple Create ML and Azure Machine Learning Studio. You will apply these solutions by using both open source and Cloud AutoML technology.
Emerging Topics in Machine Learning
This week, you will learn MLOps strategies and best practices in designing Cloud solutions. Then, you will explore Edge Machine Learning and how to use AI APIs. You will apply these strategies to build a low code or no code Cloud solution that performs Natural Language Processing or Computer Vision.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Demystifies AI APIs that connect AI models to applications and provides useful insights
Covers MLOps strategies and best practices in designing Cloud solutions
Develops proficiency in using AutoML to solve problems efficiently, which is an invaluable skill in today's job market
Builds a strong foundation for beginners in Machine Learning Engineering and its applications
Taught by Noah Gift, who is an experienced Machine Learning engineer and researcher
Save this course
Save Cloud Machine Learning Engineering and MLOps to your list so you can find it easily later:
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Reviews summary
Ml engineering and mlops fundamentals
Learners say this is a well-structured course that provides a solid foundation in Cloud Machine Learning Engineering and MLOps with its concise yet complete content. The engaging assignments and practical demonstrations help learners to develop a comprehensive understanding of AWS, GCP, and Azure MLOps solutions.
Knowledgeable and engaging
"Amazing teacher and perfect mixture of necessary informations."
"It was a privilage to learn from him, i recommend this course for every ML Engineer."
Well-organized and hands-on
"Really enjoyed the whole specialization!"
"This very hands-on and to-the-point approach was fantastic."
"Nice content and complete due that the course show the three main/popular options for MLOPs solutions: AWS, GCP and Azure"
Complete and insightful
"Insightful, complete, in detail."
"Excellent course, very concise but complete"
"Great Intro into DevOps and MLOps for beginners, Also good explanation and practical application examples"
Some repetitiveness
"The course is an introduction with a lot of repetitions from other courses of this specialization."
"Once again, disappointing repetitions."
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 Python fundamentals
Show steps
Refreshes the basics of Python language, which is used extensively in the course.
Browse courses on
Python
Show steps
-
Review Python data types, variables, and control flow
-
Practice writing simple Python functions
-
Solve beginner-level Python coding problems
Review 'Machine Learning Engineering' by Andriy Burkov
Show steps
Gain insights from an industry expert to deepen your understanding of Machine Learning Engineering principles.
View
Machine Learning Engineering
on Amazon
Show steps
-
Obtain a copy of 'Machine Learning Engineering by Andriy Burkov''.
-
Read the book, taking notes and highlighting key concepts.
-
Apply the concepts learned to your Machine Learning projects.
Compile Machine Learning Resources
Show steps
Organize and expand your collection of Machine Learning resources to enhance your understanding and efficiency.
Browse courses on
Machine Learning Tools
Show steps
-
Gather online tutorials, articles, and videos on Machine Learning
-
Bookmark or save valuable Machine Learning websites and tools
-
Create a digital or physical repository for your collection
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Learn about Machine Learning Engineering best practices
Show steps
Provides understanding of the methodologies and best practices used in Machine Learning Engineering, which is essential for developing robust and scalable solutions.
Browse courses on
Machine Learning Engineering
Show steps
-
Follow tutorials on software development principles and methodologies
-
Explore best practices for designing, implementing, and testing Machine Learning models
-
Apply these concepts to a small-scale Machine Learning project
Follow tutorials on Edge Machine Learning
Show steps
Expand your knowledge of Edge Machine Learning by following guided tutorials that provide practical instructions on how to implement Edge Machine Learning solutions for various applications.
Show steps
-
Identify relevant tutorials
-
Follow the tutorials step-by-step
-
Apply the concepts to your own projects
Review Python Skills
Show steps
Strengthen your Python programming skills to enhance your ability to apply Machine Learning Engineering concepts.
Browse courses on
Python
Show steps
-
Review Python syntax and data structures
-
Practice writing basic Python programs
-
Complete Python programming exercises or challenges
Create a resource hub
Show steps
Organize and develop a compilation of helpful resources, such as articles, tutorials, and code snippets, to support your learning of Machine Learning Engineering concepts and best practices.
Browse courses on
MLOps
Show steps
-
Identify relevant resources
-
Categorize and organize the resources
-
Create a central repository
Explore Machine Learning Best Practices
Show steps
Deepen your understanding of Machine Learning Engineering best practices to create effective and maintainable applications.
Show steps
-
Follow online tutorials on Machine Learning best practices
-
Read articles and documentation on software development best practices
-
Apply best practices in your own Machine Learning Engineering projects
Practice AutoML Techniques
Show steps
Gain hands-on experience with AutoML techniques to efficiently solve Machine Learning problems.
Browse courses on
AutoML
Show steps
-
Complete AutoML tutorials or workshops
-
Use AutoML platforms or tools to solve real-world problems
-
Experiment with different AutoML algorithms and models
Practice implementing AutoML solutions
Show steps
Reinforce your understanding of AutoML by working through practical exercises that involve implementing AutoML solutions for various use cases.
Browse courses on
AutoML
Show steps
-
Choose a dataset and problem statement
-
Select an appropriate AutoML technology
-
Train and evaluate the AutoML model
-
Optimize and deploy the model
Practice using AutoML tools
Show steps
Develops proficiency in using AutoML tools to automate the building of Machine Learning models, enabling faster and more efficient problem-solving.
Browse courses on
AutoML
Show steps
-
Experiment with different AutoML platforms, such as Google AutoML and Azure Machine Learning Studio
-
Build and evaluate AutoML models on real-world datasets
-
Compare the performance of AutoML models with manually developed models
Build a Flask Web Application
Show steps
Develop a Flask web application that serves Machine Learning predictions to strengthen your understanding of the practical implementation of Machine Learning Engineering concepts.
Browse courses on
Flask
Show steps
-
Set up a development environment for Flask
-
Create a Flask application
-
Integrate a Machine Learning model into the Flask application
-
Deploy the Flask application
Create a blog post or article on MLOps
Show steps
Deepen your understanding of MLOps by writing a blog post or article that explores the principles and best practices of MLOps and its impact on Machine Learning projects.
Browse courses on
MLOps
Show steps
-
Research and gather information
-
Outline and structure the content
-
Write the blog post or article
-
Edit and proofread
-
Publish and promote
Contribute to Open Source Machine Learning Projects
Show steps
Gain practical experience and collaborate on real-world Machine Learning projects.
Browse courses on
Machine Learning Projects
Show steps
-
Explore open source platforms and repositories hosting Machine Learning projects.
-
Identify projects that align with your interests and skill level.
-
Join the project's community, engage in discussions, and report bugs.
Build a Flask web application for Machine Learning predictions
Show steps
Apply your knowledge of Machine Learning Engineering and software development to create a real-world application that uses Flask to serve out Machine Learning predictions.
Browse courses on
Flask
Show steps
-
Design the application architecture
-
Implement the Flask web server
-
Integrate the Machine Learning model
-
Deploy and test the application
Review Python fundamentals
Show steps
Refreshes the basics of Python language, which is used extensively in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python data types, variables, and control flow
- Practice writing simple Python functions
- Solve beginner-level Python coding problems
Review 'Machine Learning Engineering' by Andriy Burkov
Show steps
Gain insights from an industry expert to deepen your understanding of Machine Learning Engineering principles.
View
Machine Learning Engineering
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of 'Machine Learning Engineering by Andriy Burkov''.
- Read the book, taking notes and highlighting key concepts.
- Apply the concepts learned to your Machine Learning projects.
Compile Machine Learning Resources
Show steps
Organize and expand your collection of Machine Learning resources to enhance your understanding and efficiency.
Browse courses on
Machine Learning Tools
Show steps
Show steps
Show steps
- Gather online tutorials, articles, and videos on Machine Learning
- Bookmark or save valuable Machine Learning websites and tools
- Create a digital or physical repository for your collection
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Learn about Machine Learning Engineering best practices
Show steps
Provides understanding of the methodologies and best practices used in Machine Learning Engineering, which is essential for developing robust and scalable solutions.
Browse courses on
Machine Learning Engineering
Show steps
Show steps
Show steps
- Follow tutorials on software development principles and methodologies
- Explore best practices for designing, implementing, and testing Machine Learning models
- Apply these concepts to a small-scale Machine Learning project
Follow tutorials on Edge Machine Learning
Show steps
Expand your knowledge of Edge Machine Learning by following guided tutorials that provide practical instructions on how to implement Edge Machine Learning solutions for various applications.
Show steps
Show steps
Show steps
- Identify relevant tutorials
- Follow the tutorials step-by-step
- Apply the concepts to your own projects
Review Python Skills
Show steps
Strengthen your Python programming skills to enhance your ability to apply Machine Learning Engineering concepts.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax and data structures
- Practice writing basic Python programs
- Complete Python programming exercises or challenges
Create a resource hub
Show steps
Organize and develop a compilation of helpful resources, such as articles, tutorials, and code snippets, to support your learning of Machine Learning Engineering concepts and best practices.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Identify relevant resources
- Categorize and organize the resources
- Create a central repository
Explore Machine Learning Best Practices
Show steps
Deepen your understanding of Machine Learning Engineering best practices to create effective and maintainable applications.
Show steps
Show steps
Show steps
- Follow online tutorials on Machine Learning best practices
- Read articles and documentation on software development best practices
- Apply best practices in your own Machine Learning Engineering projects
Practice AutoML Techniques
Show steps
Gain hands-on experience with AutoML techniques to efficiently solve Machine Learning problems.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Complete AutoML tutorials or workshops
- Use AutoML platforms or tools to solve real-world problems
- Experiment with different AutoML algorithms and models
Practice implementing AutoML solutions
Show steps
Reinforce your understanding of AutoML by working through practical exercises that involve implementing AutoML solutions for various use cases.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Choose a dataset and problem statement
- Select an appropriate AutoML technology
- Train and evaluate the AutoML model
- Optimize and deploy the model
Practice using AutoML tools
Show steps
Develops proficiency in using AutoML tools to automate the building of Machine Learning models, enabling faster and more efficient problem-solving.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Experiment with different AutoML platforms, such as Google AutoML and Azure Machine Learning Studio
- Build and evaluate AutoML models on real-world datasets
- Compare the performance of AutoML models with manually developed models
Build a Flask Web Application
Show steps
Develop a Flask web application that serves Machine Learning predictions to strengthen your understanding of the practical implementation of Machine Learning Engineering concepts.
Browse courses on
Flask
Show steps
Show steps
Show steps
- Set up a development environment for Flask
- Create a Flask application
- Integrate a Machine Learning model into the Flask application
- Deploy the Flask application
Create a blog post or article on MLOps
Show steps
Deepen your understanding of MLOps by writing a blog post or article that explores the principles and best practices of MLOps and its impact on Machine Learning projects.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Research and gather information
- Outline and structure the content
- Write the blog post or article
- Edit and proofread
- Publish and promote
Contribute to Open Source Machine Learning Projects
Show steps
Gain practical experience and collaborate on real-world Machine Learning projects.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Explore open source platforms and repositories hosting Machine Learning projects.
- Identify projects that align with your interests and skill level.
- Join the project's community, engage in discussions, and report bugs.
Build a Flask web application for Machine Learning predictions
Show steps
Apply your knowledge of Machine Learning Engineering and software development to create a real-world application that uses Flask to serve out Machine Learning predictions.
Browse courses on
Flask
Show steps
Show steps
Show steps
- Design the application architecture
- Implement the Flask web server
- Integrate the Machine Learning model
- Deploy and test the application
Career center
Learners who complete Cloud Machine Learning Engineering and MLOps will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to extract insights. This course may be useful to Data Scientists because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers become experts in building, testing, and deploying Machine Learning solutions. This course may be useful to Machine Learning Engineers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful to Software Engineers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Data Analyst
Data Analyst
Data Analysts clean, analyze, and interpret data to extract insights. This course may be useful to Data Analysts because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Data Engineer
Data Engineer
Data Engineers design, develop, and maintain data pipelines. This course may be useful to Data Engineers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Cloud Architect
Cloud Architect
Cloud Architects design, develop, and maintain cloud computing solutions. This course may be useful to Cloud Architects because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to make informed business decisions. This course may be useful to Business Intelligence Analysts because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
DevOps Engineer
DevOps Engineer
DevOps Engineers ensure that software is deployed and maintained efficiently. This course may be useful to DevOps Engineers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Data Science Manager
Data Science Manager
Data Science Managers manage teams of Data Scientists. This course may be useful to Data Science Managers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain AI systems. This course may be useful to Artificial Intelligence Engineers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Cloud Developer
Cloud Developer
Cloud Developers design, develop, and maintain cloud-based applications. This course may be useful to Cloud Developers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new Machine Learning algorithms and techniques. This course may be useful to Machine Learning Researchers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Machine Learning Product Manager
Machine Learning Product Manager
Machine Learning Product Managers manage the development and launch of Machine Learning products. This course may be useful to Machine Learning Product Managers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Artificial Intelligence Product Manager
Artificial Intelligence Product Manager
Artificial Intelligence Product Managers manage the development and launch of AI products. This course may be useful to Artificial Intelligence Product Managers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
Cloud Product Manager
Cloud Product Manager
Cloud Product Managers manage the development and launch of cloud products. This course may be useful to Cloud Product Managers because it helps build a foundation for applying Machine Learning Engineering to real-world projects. The course also covers various topics in Machine Learning, including AutoML and MLOps.
For more career information including salaries, visit:
OpenCourser.com/course/47wowm/cloud
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Cloud Machine Learning Engineering and MLOps.
Save
This textbook provides a comprehensive overview of deep learning theory and practice.
Focuses specifically on AutoML, providing practical guidance on how to use AutoML tools and techniques to build and deploy machine learning models.
Provides a comprehensive guide to deep learning theory and practice in Python.
Provides hands-on experience with popular machine learning libraries in Python.
Provides a practical introduction to machine learning using the R programming language.
Provides a comprehensive overview of Python libraries and techniques for machine learning.
Provides a practical introduction to deep learning using the Fastai library.
Gentle introduction to machine learning concepts and terminology.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/47wowm/cloud
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Effort
3 weeks of study, 3-5 hours/week
Level
Intermediate
Via
Coursera
Institution
Duke University
Instructor
Noah Gift
Language
English
Transcripts
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This course belongs to a
collection
called
Building Cloud Computing Solutions at Scale. It's comprised of four courses:
- Cloud Machine Learning Engineering and MLOps (viewing)
- Cloud Virtualization, Containers and APIs
- Cloud Data Engineering
- Cloud Computing Foundations
Good to know
Demystifies AI APIs that connect AI models to applications and provides useful insights
Covers MLOps strategies and best practices in designing Cloud solutions
Develops proficiency in using AutoML to solve problems efficiently, which is an invaluable skill in today's job market
Builds a strong foundation for beginners in Machine Learning Engineering and its applications
Taught by Noah Gift, who is an experienced Machine Learning engineer and researcher
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