Learn how to measure, monitor and control your AI Models
You worked hard to develop a collection of AI models and now you are ready to deploy them. Before you rush to deploy your application, let me ask you important questions.
What you’ll learn
- It identifies the need for an AI model to meet its stated business objectives. It defines the dimensions for these business objectives..
- It establishes the guard rails and controls to monitor AI models during deployment and production of the associated IT system..
- It details the AI model measurement framework and associated control points. It uses examples to show how framework is established for real time Applications..
- It defines what it takes for a good AI-based solution engages and “Wows” the users?.
- It proposes the skills, processes and organization to support AI Governance..
Course Content
- Introduction –> 1 lecture • 9min.
- AI Governance – Components –> 1 lecture • 15min.
- Continuous Learning Process –> 1 lecture • 15min.
- Model Measurements –> 1 lecture • 16min.
- Model Management –> 1 lecture • 13min.
- Collaboration Strategies –> 1 lecture • 17min.
- Summary –> 2 lectures • 11min.
Requirements
You worked hard to develop a collection of AI models and now you are ready to deploy them. Before you rush to deploy your application, let me ask you important questions.
- How do we sense and interpret a poorly working AI system? Presumably, you have developed a high-quality decision-making engine. Have you provided a mechanism to instrument your AI system as system gets deployed and learns from its use? What is your criteria for a good system and when do you alert your management if the AI system starts performing poorly.
- Your AI system is deployed in a 24X7 environment where users are using it round the clock. What is your mechanism to fix the problems without tearing down the system. Can you develop a continuous learning module of your system which constantly learns from use, adjusts the model and provides sufficient logs to your contributing experts to monitor its learning.
- The model starts to learn and now your experts are baffled. The learning is exactly not what they designed in the original system and now as they redesign their model, they have differences in opinion. How do you reconcile these differences and bring the best of your corporation.
- Is there a tool or a system for life cycle management and can you employ that tool to monitor your AI models like the way you developed process monitoring tools for your governed processes.
You may have many more questions. AI Governance is an evolving topic and not much material is available on this side.
However, most of our IT and Business professionals understand Data and Process governance. This course will help them extend their knowledge to include AI governance. This course will provide you an overview of how above questions can be addressed by a combination of process, skills and tools.
This course has been developed for working business professionals and project executives / management teams involved in interested in getting involved in AI engagements for their enterprises
In this course, you will learn
- Does an AI based solution meets its stated objectives?
- How to put control and guard-rails for an AI based solution
- How does your model perform while retaining its value accuracy and integrity?
- Does a good AI-based solution engages and “Wows” the users?
- What skills and tools are available to address some of the governance challenges?