Cloud Machine Learning Engineering and MLOps from edX

What's inside

Learning objectives

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

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:

Module 3 (5 hours)

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)

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)

Managed Machine Learning Systems (10 minutes)

Use Apple's AutoML Computer Vision (10 minutes)

Week 2 Quiz (30 minutes)

Impact of AutoML? (10 minutes)

Open Source AutoML (10 minutes)

ML Studio Products (10 minutes)

Emerging Topics in Machine Learning

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)

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)

Week 3 Quiz (30 minutes)

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)

Pickle an ML Model (60 minutes)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches best practices in machine learning engineering, which is crucial for professionals in the field

Develops expertise in continuous delivery pipelines, empowering learners to build scalable ML systems

Provides hands-on experience with industry-standard AutoML solutions, enhancing learners' abilities to efficiently train models

Explores cutting-edge topics such as MLOps and edge machine learning, preparing learners for the future of ML engineering

Covers diverse aspects of ML engineering, making it suitable for learners with various backgrounds

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 and Organize Course Notes

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Regularly reviewing and organizing your course notes will help you retain the information better and improve your ability to recall it during assessments.

Browse courses on Note-Taking

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After each lecture or module, set aside time to review your notes and identify key concepts.
Organize your notes into a logical structure, such as by topic or chronological order.
Highlight or color-code important sections to make them easier to find.
Use a note-taking app or digital notebook for ease of organization and accessibility.

Form a Study Group with Classmates

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Forming a study group with classmates will provide you with a supportive environment to discuss concepts, share resources, and work through problems together.

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Reach out to classmates via email or online forums to gauge interest.
Set up regular meeting times and a communication platform (e.g., Zoom, Slack).
Establish clear roles and expectations for each member.
Meet consistently and focus on actively engaging with the course material.

Review Command Line Basics

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Reviewing the basics of the command line will prepare you for tasks where you need to navigate the command line in future modules.

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Read a tutorial on basic command line commands.
Open a terminal window or command prompt.
Practice running basic commands like 'ls' and 'cd' to navigate the file system.
Experiment with pipes and redirection using commands like 'grep' and '>'

Four other activities

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Create a Simple Flask API Using a Tutorial

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Following a tutorial to create a simple Flask API will give you hands-on experience with a key framework used in building scalable machine learning applications.

Browse courses on RESTful APIs

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Find a beginner-friendly tutorial on creating a Flask API.
Set up a development environment with Python and Flask installed.
Follow the tutorial step-by-step to create a basic API with endpoints and data handling.
Test the API using a tool like Postman or curl.

Solve LeetCode Problems on Machine Learning Algorithms

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Solving LeetCode problems on machine learning algorithms will help you practice implementing and applying machine learning concepts in a practical setting.

Browse courses on Problem Solving

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Sign up for a LeetCode account.
Choose problems related to machine learning algorithms like classification or regression.
Solve the problems in your preferred programming language.
Review solutions and common approaches to gain insights and improve your problem-solving skills.

Create a Glossary of Key Machine Learning Concepts

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Creating a glossary of key machine learning concepts will help you solidify your understanding of the terminology and technical vocabulary used in the field.

Browse courses on Glossary Creation

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Review the course readings and materials to identify important concepts.
Define each concept clearly and concisely.
Organize the glossary alphabetically or by category.
Review and expand the glossary regularly.

Contribute to an Open-Source AutoML Project

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Contributing to an open-source AutoML project will give you practical experience working with AutoML tools and contribute to the development of the field.

Browse courses on AutoML

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Identify an open-source AutoML project on platforms like GitHub or GitLab.
Review the project's documentation and contribution guidelines.
Choose an area where you can make a meaningful contribution, such as bug fixes, feature enhancements, or documentation improvements.
Submit a pull request with your changes and follow the project's review process.

Career center

Learners who complete Cloud Machine Learning Engineering and MLOps will develop knowledge and skills that may be useful to these careers:

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Cloud Machine Learning Engineering and MLOps

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