Managing Machine Learning Projects with Google Cloud (Coursera)

Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.

What You Will Learn

• Assess the feasibility of your own ML use case and its ability to meaningfully impact your business.
• Identify the requirements to build, train, and evaluate an ML model.
• Define data characteristics and biases that affect the quality of ML models.
• Recognize key considerations for managing ML projects.

Course 3 of 3 in the Digital Transformation Using AI/ML with Google Cloud Specialization

Syllabus

WEEK 1
Module 1: Introduction
Welcome to the course! In this module, you'll meet the instructor and learn about the course content and how to get started.
Module 2: Identifying business value for using ML
This module begins by defining machine learning at a high level and then helps you gain a thorough understanding of its value for business by reviewing several real-world examples. It then introduces machine learning projects and provides practice using a tool to assess the feasibility of several ML problems.

WEEK 2
Module 3: Defining ML as a practice
There are several types of machine learning problems. In this module, you'll learn to differentiate between the most common ones; develop the key vocabulary to support yourself when working with ML experts; practice categorizing various examples of ML problems; and identify the short- and long-term benefits when solving those ML problems.
Module 4: Building and evaluating ML models
After you have assessed the feasibility of your supervised ML problem, you're ready to move to the next phase of an ML project. This module explores the various considerations and requirements for building a complete dataset in preparation for training, evaluating, and deploying an ML model. It also includes two demos—Vision API and AutoML Vision—as relevant tools that you can easily access yourself or in partnership with a data scientist. You'll also have the opportunity to try out AutoML Vision with the first hands-on lab.

WEEK 3
Module 5: Using ML responsibly and ethically
Data in the world is inherently biased, and that bias can be amplified through ML solutions. In this module, you'll learn about some of the most common biases and how they can disproportionately affect or harm an individual or groups of individuals. You'll also be given guidelines for uncovering possible biases at each phase of an ML project and strategies for achieving ML fairness as much as possible.
Module 6: Discovering ML use cases in day-to-day business
This module explores 5 general themes for discovering ML use cases within day-to-day business, followed by concrete customer examples. You'll learn about creative applications of ML, such as improving the resolution of images or generating music.

WEEK 4
Module 7: Managing ML projects successfully
When you thoroughly understand the fundamentals of machine learning and considerations within in each phase of the project, you're ready to learn about the best practices for managing an ML project. This module describes 5 key considerations for successfully managing an ML project end-to-end: identifying the business value, developing a data strategy, establishing data governance, building successful ML teams, and enabling a culture of innovation. You'll also have an opportunity to gain further exposure to one of Google Cloud's tools by completing a final hands-on lab: Evaluate an ML Model with BigQuery ML.
Module 8: Summary
This module provides a summary of the key points covered in each of the modules in the course.