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Managing ML Projects shows you how to manage an ML project as it progresses
from an idea to a production-ready implementation. The course covers the
ML development phases and the roles and skills
typically found on ML teams. It discusses strategies for working with
stakeholders and provides details on how to plan and manage an ML project
at each phase of development.
By demystifying the complexities inherent in ML projects, the course
provides a solid theoretical framework for managing ML projects.
The course focuses on traditional ML models. Although generative AI is in the
spotlight, traditional ML plays a vital role at Google, underpinning many
services and projects, from predicting travel times in Maps to estimating the
price of airline tickets in Flights, from predicting compute quota for Google
Cloud customers to recommending relevant videos in YouTube.
In general, the principles for managing traditional ML projects are identical
for managing generative AI projects. When there's a significant difference, the
course provides relevant generative AI advice and guidance.
You should first verify that ML is the right approach for your problem.
If you haven't framed your problem in terms of an ML solution, complete
Introduction to Machine
Learning Problem Framing.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-09-18 UTC."],[[["This course provides a comprehensive framework for managing machine learning (ML) projects, guiding you through all stages from ideation to production."],["It covers key aspects such as defining project phases, planning and management strategies, establishing success metrics, and implementing responsible AI practices."],["While focused on traditional ML models, the course also offers insights into managing generative AI projects, highlighting common principles and key differences."],["To benefit from this course, you should have a basic understanding of machine learning and have already determined that ML is the appropriate solution for your problem."],["It's estimated to take approximately 90 minutes to complete this course, equipping you with the necessary skills to effectively manage your ML projects."]]],[]]