Managing Machine Learning Projects with Google Cloud
Master the art of translating real-life business problems into machine learning use cases, vetting them for feasibility and impact before conveying the requirements to other teams on this two-day course.
Book this courseCourse Overview
2 day course
Certificate of Attendance
Private
Partner of the Year
As a Google Cloud Partner, Jellyfish provides world-leading Cloud-based training solutions to help clients succeed.
As a Google Cloud Partner, Jellyfish has been selected to deliver this course, which is aimed at business professionals in non-technical roles who are looking to lead or influence machine learning projects.
During the session, you’ll delve into machine learning, while bypassing all the technical jargon. You’ll learn how to translate business problems into custom machine learning use cases, assess each phase of the project and translate the requirements to your technical team.
Our Managing Machine Learning Projects with Google Cloud course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in the UK.
What you'll learn
By the end of this course, you will be able to:
Understand how ML can be used to improve business processes and create new value
Identify the requirements to carry out an ML project, from assessing feasibility, to data preparation, to model training, to evaluation, to deployment
Recognize key considerations for managing ML projects including data strategy, governance, and project teams
Explore common machine learning use cases implemented by businesses
Define data characteristics and biases that affect the quality of ML models
Pitch a custom ML use case that can meaningfully impact your business
Course agenda
Module 1: Introduction
- Differentiate between AI, machine learning, and deep learning
- Describe the high-level uses of ML to improve business processes or to create new value
- Begin assessing the feasibility of ML use cases
Module 2: What is Machine Learning?
- Differentiate between supervised and unsupervised machine learning problem types
- Identify examples of regression, classification, and clustering problem statements
- Recognize the core components of Google’s standard definition for ML and considerations for each when carrying out an ML project
Module 3: Employing ML
- Describe the end-to-end process to carry out an ML project and considerations within each phase
- Practice pitching a custom ML problem statement that has the potential to meaningfully impact your business
Module 4: Discovering ML Use Cases
- Discover common machine learning opportunities in day-to-day business processes
Module 5: How to be Successful at ML
- Identify the requirements for businesses to successfully use ML
Module 6: Summary
- Summarize key concepts and tools covered in the course content
- Compete for best ML use case presentation based on creativity, originality, and feasibility
Who it's for
This course is suitable for enterprise, corporate, or SMB business professionals in technical roles. You may be a business analyst, IT manager, project manager, or product manager. We recommend that VPs and above attend the Data-driven Transformation with Google Cloud course instead.
Prerequisites
No prior technical knowledge is required to attend this course, but you should be knowledgeable about your own business and objectives. We also recommend completing the Business Transformation with Google Cloud course beforehand.
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