This course is part of the Building Cloud Computing Solutions at Scale Specialization

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Noah Gift

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Offered By

Building Cloud Computing Solutions at Scale SpecializationDuke University

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

Building Cloud Computing Solutions at Scale Specialization

Intermediate Level

Beginner level Linux and Python skills

Approx. 12 hours to complete

English

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

Building Cloud Computing Solutions at Scale Specialization

Intermediate Level

Beginner level Linux and Python skills

Approx. 12 hours to complete

English

Instructor

Instructor rating

4.64/5 (15 Ratings)

Noah Gift

Executive in Residence and Founder of Pragmatic AI Labs

Interdisciplinary Data Science (MIDS)

44,599 Learners

12 Courses

Offered by

Duke University

Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

Getting Started with Machine Learning Engineering

4 hours to complete

15 videos (Total 47 min), 5 readings, 1 quiz

15 videos

Instructor Introduction1mCourse Introduction2mLab Onboarding1mCourse 4 Project Overview1mIntroduction to Machine Learning Engineering30sMachine Learning Engineering Overview1mMachine Learning Engineering Architecture3mIntroduction to Machine Learning Microservices40sMachine Learning Microservices Overview1mMonolithic versus Microservice2mIntroduction to Continuous Delivery for Machine Learning39sContinuous Delivery for Machine Learning Overview1mWhat is Data Drift?2mContinuously Deploy Flask ML Application4mAWS App Runner: High-Level PaaS Continuous Delivery21m

5 readings

Specialization Project Roadmap: Course 410mCourse Structure and Discussion Etiquette10mJupyter Notebook Workflow for Machine Learning10mK-Means Clustering Sample Dataset10mHigh Level MLOps Continuous Deployment10m

1 practice exercise

Week 1 Quiz30m

Week2

Week 2

4 hours to complete

Using AutoML

4 hours to complete

21 videos (Total 138 min), 2 readings, 1 quiz

21 videos

Introduction to AutoML50sWhat is AutoML?1mAutoML Computer Vision3mIntroduction to No Code/Low Code4mNo Code/Low Code AutoML: Part 134mNo Code/Low Code AutoML: Part 218mApple Create ML AutoML19mIntroduction to Ludwig AutoML1mWhat is Ludwig AutoML?1mLudwig AutoML Deep Dive2mLudwig AutoML By Example5mIntroduction to Cloud AutoML46sWhat is Cloud AutoML?1mCloud AutoML Deep Dive1mGuest Speaker: Alfredo Deza1mIntroduction to Azure Machine Learning Studio3mCreate a Dataset in Azure Machine Learning Studio10mAutomated ML Run in Azure Machine Learning Studio12mExperiments in Azure Machine Learning Studio3mDeploy a Module in Azure Machine Learning Studio5mTest Endpoints in Azure Machine Learning Studio4m

2 readings

Managed Machine Learning Systems10mUse Apple's AutoML Computer Vision10m

1 practice exercise

Week 2 Quiz30m

Week3

Week 3

5 hours to complete

Emerging Topics in Machine Learning

5 hours to complete

22 videos (Total 158 min), 2 readings, 1 quiz

22 videos

Introduction to MLOps38sWhat is MLOps?1mMLOps Deep Dive3mIntroduction to Edge Machine Learning49sWhat is Edge Machine Learning?3mEdge Machine Learning Vision in Action6mHardware Inference Model Solutions in Edge Machine Learning23mEdge Machine Learning in Google29mEdge Machine Learning in AWS16mIntroduction to AI APIs57sHow to Use AI APIs?2mCore Components of a Cloud Application4mAWS Comprehend for Natural Language Processing7mAWS Rekognition for Computer Vision2mGCP AutoML for Natural Language Processing10mGCP AutoML for Computer Vision4mAzure AutoML for AI Predictions16mAzure AutoML for Computer Vision1mCore Components of a Cloud Application Recap34sSteps to Developing an API9mFlask Machine Learning Backend4mChecklist for Building Professional Web Services7m

2 readings

Deep Dive: Use a Low Code or No Code Cloud AI API to Solve a Problem10mDeploy a Flask Machine Learning Model That You Didn't Build10m

1 practice exercise

Week 3 Quiz30m

Reviews

4.6

12 reviews

5 stars
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4 stars
16.66%
3 stars
8.33%
2 stars
3.33%

TOP REVIEWS FROM CLOUD MACHINE LEARNING ENGINEERING AND MLOPS

by JYJun 2, 2022

Great course to know practical ideas and concepts.

by ADNov 1, 2022

Great Intro into DevOps and MLOps for beginners, Also good explanation and practical application examples

by YAFeb 8, 2022

Amazing teacher and perfect mixture of necessary informations. It was a privilage to learn from him, i recommend this course for every ML Engineer.

by SGJul 9, 2021

Excellent course, very concise but complete, if possible a second version would be ideal

View all reviews

About the Building Cloud Computing Solutions at Scale Specialization

With more companies leveraging software that runs on the Cloud, there is a growing need to find and hire individuals with the skills needed to build solutions on a variety of Cloud platforms. Employers agree: Cloud talent is hard to find. This Specialization is designed to address the Cloud talent gap by providing training to anyone interested in developing the job-ready, pragmatic skills needed for careers that leverage Cloud-native technologies.

In the first course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines, using the best practices of DevOps. The second course will teach you how to build effective Microservices using technologies like Flask and Kubernetes that are continuously deployed to a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP). The third course begins to put together all of the Cloud concepts introduced in the first two courses to tackle more complex data engineering solutions. And finally, in the fourth course you will apply Machine Learning Engineering to build a Flask web application that serves out Machine Learning predictions.

Building Cloud Computing Solutions at Scale

Frequently Asked Questions

When will I have access to the lectures and assignments?
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:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Cloud Machine Learning Engineering and MLOps

About this Course