Create and Execute Successful Machine Learning (ML) Programs

Description

This course summarizes Artificial Intelligence (AI) and Machine Learning (ML) for business leaders and lists ways in which AI/ML is used in business across several industries today.

In the first part, the course discusses the foundations of setting up a successful AI/ML strategy in the enterprise, considering business value, availability of historical data, and feasibility or path to production. We will use foundational strategic pillars to help define a complete strategy and look at the factors that we need to consider when implementing AI/ML.

In the second part, we will look at some mechanisms to manage the specific challenges when implementing ML, given the unknown we are facing in these types of programs, such as a possible time consuming Exploratory Data Analysis, a pivot in scope as we understand the data better, and so forth.

We also propose a risk management framework that quantifies risks and impacts, together with an overseeing business unit. In this context, we will focus on understanding Responsible AI and how it is a foundational part of any AI/ML use case that affects people in any way. It is the responsibility of business leaders to ensure the ML models they shepherd in front of their customers are fair and unbiased, and any decision taken can be explained.

What You Will Learn!

• Understand the business value that Artificial Intelligence and Machine Learning can bring
• Establish the pillars of a successful AI strategy
• Execute the ML program using Agile to avoid common pitfalls
• Learn a framework to manage risks in ML programs
• What is Responsible AI and how to ensure your ML program is compliant

Who Should Attend!

• Business Leaders who want to understand what AI and ML are and how to implement a successful AI strategy
• Project/Program Managers who work with Machine Learning use cases

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