Managing Machine Learning Projects with Google Cloud

• 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 quality of ML models.
• Recognize 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.

• No prior technical knowledge is required.
• Saavy about your own business and objectives.
• Recommended: completing the Business Transformation with Google Cloud course.

2 days – 16 hours

The course includes presentations, demonstrations, and immersive activities.

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.
• Personalizing 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, feedback form.