Managing Machine Learning Projects with Google Cloud

Name: Managing Machine Learning Projects with Google Cloud
Price: 1000.00 ZAR

Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact.

2 day course

Supporting material

Google Cloud Partner of the Year

Private

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

Prerequisites

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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