Unleash the power of machine learning with engineering best practices, AutoML, and emerging technologies.
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- 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.
This course is part of the Introduction to Cloud Computing Professional Certificate.
What you'll learn
- 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)
Course Introduction (2 minutes)
Lab Onboarding (1 minute)
Course 4 Project Overview (1 minute)
Introduction to Machine Learning Engineering (0 minutes)
Machine Learning Engineering Overview (1 minute)
Machine Learning Engineering Architecture (3 minutes)
Introduction to Machine Learning Microservices (0 minutes)
Machine Learning Microservices Overview (1 minute)
Monolithic versus Microservice (2 minutes)
Introduction to Continuous Delivery for Machine Learning (0 minutes)
Continuous Delivery for Machine Learning Overview (1 minute)
What is Data Drift? (2 minutes)
Continuously Deploy Flask ML Application (4 minutes)
AWS App Runner: High-Level PaaS Continuous Delivery (21 minutes)
Readings:
Specialization Project Roadmap: Course 4 (10 minutes)
Course Structure and Discussion Etiquette (10 minutes)
Jupyter Notebook Workflow for Machine Learning (10 minutes)
K-Means Clustering Sample Dataset (10 minutes)
High Level MLOps Continuous Deployment (10 minutes)
Quiz:
Week 1 Quiz (30 minutes)
Discussion Prompts:
Introductions (10 minutes)
Microservices in MLOps (10 minutes)
PaaS (Platform as a Service) and MLOPs (10 minutes)
Ungraded Lab:
Flask Machine Learning Microservice (60 minutes)
Using AutoML
Module 2 (3 hours)
Videos:
Introduction to AutoML (0 minutes)
What is AutoML? (1 minute)
AutoML Computer Vision (3 minutes)
Introduction to No Code/Low Code (4 minutes)
No Code/Low Code AutoML: Part 1 (34 minutes)
No Code/Low Code AutoML: Part 2 (18 minutes)
Apple Create ML AutoML (19 minutes)
Introduction to Ludwig AutoML (1 minute)
What is Ludwig AutoML? (1 minute)
Ludwig AutoML Deep Dive (2 minutes)
Ludwig AutoML By Example (5 minutes)
Introduction to Cloud AutoML (0 minutes)
What is Cloud AutoML? (1 minute)
Cloud AutoML Deep Dive (1 minute)
Guest Speaker: Alfredo Deza (1 minute)
Introduction to Azure Machine Learning Studio (3 minutes)
Create a Dataset in Azure Machine Learning Studio (10 minutes)
Automated ML Run in Azure Machine Learning Studio (12 minutes)
Experiments in Azure Machine Learning Studio (3 minutes)
Deploy a Module in Azure Machine Learning Studio (5 minutes)
Test Endpoints in Azure Machine Learning Studio (4 minutes)
Readings:
Managed Machine Learning Systems (10 minutes)
Use Apple's AutoML Computer Vision (10 minutes)
Quiz:
Week 2 Quiz (30 minutes)
Discussion Prompts:
Impact of AutoML? (10 minutes)
Open Source AutoML (10 minutes)
ML Studio Products (10 minutes)
Emerging Topics in Machine Learning
Module 3 (5 hours)
Videos:
Introduction to MLOps (0 minutes)
What is MLOps? (1 minute)
MLOps Deep Dive (3 minutes)
Introduction to Edge Machine Learning (0 minutes)
What is Edge Machine Learning? (3 minutes)
Edge Machine Learning Vision in Action (6 minutes)
Hardware Inference Model Solutions in Edge Machine Learning (23 minutes)
Edge Machine Learning in Google (29 minutes)
Edge Machine Learning in AWS (16 minutes)
Introduction to AI APIs (0 minutes)
How to Use AI APIs? (2 minutes)
Core Components of a Cloud Application (4 minutes)
AWS Comprehend for Natural Language Processing (7 minutes)
AWS Rekognition for Computer Vision (2 minutes)
GCP AutoML for Natural Language Processing (10 minutes)
GCP AutoML for Computer Vision (4 minutes)
Azure AutoML for AI Predictions (16 minutes)
Azure AutoML for Computer Vision (1 minute)
Core Components of a Cloud Application Recap (0 minutes)
Steps to Developing an API (9 minutes)
Flask Machine Learning Backend (4 minutes)
Checklist for Building Professional Web Services (7 minutes)
Readings:
Deep Dive: Use a Low Code or No Code Cloud AI API to Solve a Problem (10 minutes)
Deploy a Flask Machine Learning Model That You Didn't Build (10 minutes)
Next Steps (10 minutes)
Quiz:
Week 3 Quiz (30 minutes)
Discussion Prompts:
Why MLOps? (10 minutes)
Edge Machine Learning (10 minutes)
No Code and Low Code Solutions (10 minutes)
Standards of Excellence in Software Engineering (10 minutes)
Ungraded Lab:
Pickle an ML Model (60 minutes)
