Archives: Courses

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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Data & Intellectual Property Considerations for AI

Understanding data rights, property rights, and intellectual property are important areas when it comes to AI use. In this course we’ll provide a foundational level overview of data & intellectual property (IP) considerations for AI. This course is geared towards anyone looking to gain a basic understanding on this subject.

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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CPMAI Methodology Phase I: Business Understanding

In this course we’ll walk you through Phase I of the CPMAI Methodology: Business Understanding. In this phase, we’ll help you with business understanding including answering what are your core business requirements and objectives, provide you with AI-relevant considerations, and an assessment of the business situation. You’ll gain an understanding of AI project management including …

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Best Practices & Methodologies for Successful AI Implementation

Knowing best practices for implementing AI on a large scale is critical for the success of your project. Existing methodologies that are either application development-centric or enterprise architecture focused or rooted in hardware or software development approaches face significant challenges when faced with the unique lifecycle requirements of AI projects. It’s also important to know …

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Responsible AI: Incident Response

When creating AI systems, you should have responsible AI on your mind. Operating ethically & legally is crucial to have long-lasting adoption and success with AI systems. This course will focus on how to design appropriate incident response for AI and cloud service models as well as when to escalate concerns about ethical and safety …

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Applying Responsible AI

Operating ethically & legally is crucial to have long-lasting adoption and success with AI systems. This course will provide guidance on how to implement various controls, safeguards, governance approaches, and technical issues in responsible use of AI such as measuring bias and fairness. This course is geared towards anyone who plans, builds, or deploys AI …

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Ethical & Responsible AI

For AI to have a lasting positive impact it must be done responsibly. In this course we will go over the definitions and concepts of ethical AI and responsible AI. This course outlines ethical issues related to AI and how you can adhere to all relevant regulations and emerging ethical frameworks. This course is geared …

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