Managing Machine Learning Projects with Google Cloud

Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact.
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2 day course
Supporting material
Google Cloud Partner of the Year
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

This course is for Business professionals in non-technical roles who are looking to lead or influence machine learning projects.

Jellyfish has recently been named a Google Cloud Specialisation Partner of the Year. This title recognises our commitment to provide world-leading Cloud-based Training solutions that help our clients succeed.

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 translate the requirements to a technical team.

Course overview
Who should attend:
This course is intended for the following participants:
  • Enterprise, corporate, or SMB business professionals in non-technical roles. Roles include but are not limited to: business analysts, IT managers, project managers, product managers. For senior VPs and above, Data-driven Transformation with Google Cloud is more suitable.
Walk away with the ability to:
  • Gain a thorough understanding of how ML can be used to improve business processes and create new value
  • Explore common machine learning use cases implemented by businesses
  • Identify the requirements to carry out an ML project from assessing feasibility, to data preparation, to model training, to evaluation, to deployment
  • Define data characteristics and biases that affect the quality of ML models
  • Recognise key considerations for managing ML projects including data strategy, governance, and project teams
  • Pitch a custom ML use case that can meaningfully impact your business
Participants do not need prior technical knowledge but you should be knowledgeable about your own business and objectives. Completing the Business Transformation with Google Cloud course is recommended.
Course agenda
Module 1: Introduction
  • Overview: what is machine learning?
  • Key terms: Artificial intelligence, machine learning, and deep learning
  • Real-world examples of machine learning
  • Overview: five phases in a machine learning project
  • Phase 1: Assess the ML use case for specificity and difficulty
  • Brainstorm a minimum of three custom ML use cases
Module 2: What is Machine Learning?
  • Common ML problem types
  • Standard algorithms
  • Data characteristics
  • Predictive insights and decisions
  • More real-life ML use cases
  • Why ML now?
Module 3: Employing ML
  • Features and labels
  • Building labeled data sets
  • Training an ML model
  • Evaluating an ML model
  • General best practices
  • Human bias and ML fairness
  • Part 1: custom ML use case proposal
Module 4: Discovering ML Use Cases
  • Replacing rules with machine learning
  • Automating business processes with machine learning
  • Understanding unstructured data with machine learning
  • Personalising applications with machine learning
  • Creative use cases with machine learning
Module 5: How to be Successful at ML
  • Key considerations
  • Formulating a data strategy
  • Developing governance around uses of machine learning
  • Building successful machine learning teams
  • Creating a culture of innovation
Module 6: Summary
  • Summary, presentations and feedback form
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