Duke University - Cloud Machine Learning Engineering and MLOps
- Offered byCoursera
Cloud Machine Learning Engineering and MLOps at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Cloud Machine Learning Engineering and MLOps at Coursera Highlights
- Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 4 in the Building Cloud Computing Solutions at Scale Specialization
Cloud Machine Learning Engineering and MLOps at Coursera Course details
- 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.
Cloud Machine Learning Engineering and MLOps at Coursera Curriculum
Getting Started with Machine Learning Engineering
Instructor Introduction
Course Introduction
Lab Onboarding
Course 4 Project Overview
Introduction to Machine Learning Engineering
Machine Learning Engineering Overview
Machine Learning Engineering Architecture
Introduction to Machine Learning Microservices
Machine Learning Microservices Overview
Monolithic versus Microservice
Introduction to Continuous Delivery for Machine Learning
Continuous Delivery for Machine Learning Overview
What is Data Drift?
Continuously Deploy Flask ML Application
AWS App Runner: High-Level PaaS Continuous Delivery
Specialization Project Roadmap: Course 4
Course Structure and Discussion Etiquette
Jupyter Notebook Workflow for Machine Learning
K-Means Clustering Sample Dataset
High Level MLOps Continuous Deployment
Week 1 Quiz
Using AutoML
Introduction to AutoML
What is AutoML?
AutoML Computer Vision
Introduction to No Code/Low Code
No Code/Low Code AutoML: Part 1
No Code/Low Code AutoML: Part 2
Apple Create ML AutoML
Introduction to Ludwig AutoML
What is Ludwig AutoML?
Ludwig AutoML Deep Dive
Ludwig AutoML By Example
Introduction to Cloud AutoML
What is Cloud AutoML?
Cloud AutoML Deep Dive
Guest Speaker: Alfredo Deza
Introduction to Azure Machine Learning Studio
Create a Dataset in Azure Machine Learning Studio
Automated ML Run in Azure Machine Learning Studio
Experiments in Azure Machine Learning Studio
Deploy a Module in Azure Machine Learning Studio
Test Endpoints in Azure Machine Learning Studio
Managed Machine Learning Systems
Use Apple's AutoML Computer Vision
Week 2 Quiz
Emerging Topics in Machine Learning
Introduction to MLOps
What is MLOps?
MLOps Deep Dive
Introduction to Edge Machine Learning
What is Edge Machine Learning?
Edge Machine Learning Vision in Action
Hardware Inference Model Solutions in Edge Machine Learning
Edge Machine Learning in Google
Edge Machine Learning in AWS
Introduction to AI APIs
How to Use AI APIs?
Core Components of a Cloud Application
AWS Comprehend for Natural Language Processing
AWS Rekognition for Computer Vision
GCP AutoML for Natural Language Processing
GCP AutoML for Computer Vision
Azure AutoML for AI Predictions
Azure AutoML for Computer Vision
Core Components of a Cloud Application Recap
Steps to Developing an API
Flask Machine Learning Backend
Checklist for Building Professional Web Services
Use a Low Code or No Code Cloud AI API to Solve a Problem
Deploy a Flask Machine Learning Model That You Didn't Build
Week 3 Quiz
Cloud Machine Learning Engineering and MLOps at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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