Course Category: Implementing AI

Model Operationalization

This course digs deeper into model operationalization, and the specifics for how to put models for inference into various places of deployment and operations. We’ll go over the different ways you operationalize your model including on the edge, in the cloud, on premise, and hybrid options. This intermediate course is designed for those implementing AI.

Model Evaluation and Testing

This course digs into model evaluation and testing, providing insights into key evaluation concepts like the confusion matrix, Business / KPI evaluation, learning curves, ROC and AUC curves, and evaluating AI models vs. the heuristic. This is an intermediate level course geared towards project managers and those in a more technical role such as data …

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Applying Big Data Practices to AI

Smart organizations use vast quantities of data to better understand their customers, track inventory, improve logistics and operational processes, and make better informed business decisions. Data is often stored in silos, hard to access data warehouses, or stuck in old systems that lack integration with the rest of the organization. This course shows you how …

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Data Preparation for AI

“Garbage in is garbage out” is very much the case when it comes to systems that rely on data to learn, and AI systems are certainly no exception. Without clean and accurate data you run the risk of training your AI systems on bad data, resulting in skewed or inaccurate results. In this course we’ll …

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CPMAI Methodology Phase VI: Model Operationalization

In this course we’ll walk you through phase VI of the CPMAI Methodology. In this phase, you’re focused on putting your models into operation, also called model operationalization. We discuss a deployment plan, a monitoring and maintenance plan, as well as additional considerations needed for model operationalization. You’ll gain an understanding of AI project management …

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CPMAI Methodology Phase V: Model Evaluation

In this course we’ll walk you through Phase V of the CPMAI Methodology. This phase is focused onmodel evaluation, including use of training learning curves, confusion matrix evaluation considerations, real-world model testing and validation, and other necessary steps for evaluating your model. You’ll gain an understanding of AI project management including product development & prototyping …

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