Cloud Machine Learning Engineering and MLOps

Pragmatic AI Labs via edX

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Overview

Cloud Machine Learning Engineering and MLOps is a comprehensive course designed to teach learners how to build, deploy, and maintain scalable machine learning solutions in cloud environments using best practices in MLOps (Machine Learning Operations). The course bridges the gap between data science and production engineering, focusing on automating and optimizing ML workflows to ensure reliability, scalability, and continuous improvement. The curriculum covers core concepts including cloud computing fundamentals, containerization with Docker, orchestration using Kubernetes, and CI/CD pipelines tailored for...

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)