Full Course Description
Machine learning, and the broader topics of Advanced Analytics, Artificial Intelligence, and Big Data, are continually increasing in most industries and a multitude of business areas. To accelerate growth and maintain a competitive edge, every IT leader should be considering how to leverage ML in their business.
- Business executives
- IT leaders
Participants should attend with the intent of engaging in discussion to maximize the value and applicability of the topics in this course.
Module 1: The competitive advantage of ML
Why is everyone talking about AI? Why is it important for every business, in any industry, to consider where ML fits into their strategic vision? In this module, we’ll look at the power of machine learning, use cases, terminology, and taxonomy.
- Why ML?
- What can ML do for you? What can’t it do?
- Demystifying terminology: AI, ML, Big Data
- Taxonomy and overview of ML techniques
Module 2: ML lifecycle
In this module, we’ll discuss the lifecycle of ML models, the importance of good data, and the evaluation of a model.
- The life of an ML model
- The importance of data to ML
- Long term model evaluation
- Model monitoring and maintenance
Module 3: Implementing ML
In this module, we’ll highlight the suitability of ML to different use cases, common technologies, and other considerations when adopting machine learning.
- Current technology offerings
- Bias in ML
- Interpretability of models
- Models to inform, not dictate
Module 4: ML roadmap
In this module, we’ll discuss how to evaluate the impact of machine learning and key roles and responsibilities in delivering ML projects.
- Maturity and accessibility of different ML techniques
- ML in operations and product development
- Estimating impact and priorities
- Definition of key roles and responsibilities