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The world is being transformed by artificial intelligence (AI). Many businesses and industries are implementing this technology to improve predictability, streamline processes, and stay ahead of the competition. But introducing AI software and practices isn’t always easy.

In this course, you will learn how to successfully integrate AI into any organization. You’ll learn how to determine whether your organization could fully benefit from AI, as well as the technical capabilities necessary for deployment. You’ll learn how to perform business analysis and present a case for implementation.

Further, you’ll learn project management best practices to help install AI successfully. You’ll use the Scrum framework to build and deploy AI and to maintain the system when in production to collect data and model scenarios.

AI Project Management: Deploying and Maintaining AI for Business

There is no such thing as a final version of an AI model. Our AI model’s work is never finished. Over time, our model needs to be updated and improved. This means that the data scientists must review and monitor previous project work even after the models are in production.

# Monitoring our models

This is done to ensure that the models remain accurate, however that is defined by our company. One of the things our team should consider is how to monitor the models. Let’s consider the following two questions:

- How can we ensure that our model is in good health? 

- Are the models being degraded? 

Let's go back to the e-commerce example. There are new products that need to be added to the store. Some items in the store are seasonal, and the sales may reflect this. With time, new techniques and algorithms become available, and data scientists may wish to test these seasonal items to see if they can achieve better results. One useful application is a dashboard that allows data scientists to monitor the application’s health, CPU usage, and response times.

In fact, why don’t we create a dashboard for our own artificial intelligence models? For example, if we have an e-commerce store, we can track spikes and drops in some of our metrics by asking the following questions:


- Was there anything wrong with the data?

- Can we run the most recent data set through a development model and replicate the model's behavior? 

Perhaps the model we developed necessitates certain input features, but for some reason, those inputs are missing in production. In that case, we ask these questions:

- How does the model behave in this particular case?

- What is the frequency with which a model should be retrained? 


Everything is dependent on what the models predict. The following points are worth remembering:

- External changes can always impact the accuracy of machine learning models. 

- Seasonal changes and weather influence the purchases of an online retailer.

- Marketing campaigns can affect web traffic and sales.



Let’s find out how to handle AI models in production.

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Deploying and Optimizing the Models

Monitoring our models