Introducing AI to your Organization_468 x 60 copy 2.png

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

The focus of our course is to identify what’s required to introduce AI into an organization and how to go about it. We won’t go into the technical details of creating AI models here. Instead, we’ll get an overview of how data collection and AI modeling should be incorporated into an organization.
 

# Set up cloud accounts and extract data

Setting up cloud accounts and credentials, along with extracting data from the platform, are typically the first steps in any AI project. This provides only a subset of the data to the data science team to work with while the rest is ingested.







# Have a generalist team

Having a generalist team that can share tasks within the team comes in handy. The data scientists may not have access to all of the data at the start of the project. Having a generalist team means that we will still have people who can assist with many tasks, such as ingesting some of the data from the platform into the AI system.


# Collect only the required data

One thing to keep an eye out for in projects at this stage is the data scientists’ understanding of the minimum amount of data required to create a model. It is common for data scientists to want to collect all of the data up-front only to discover that some of it is irrelevant later. And it’s critical for the data scientists, product owner, and Scrum Master to discuss and agree upon a course of action.

It is far better for the data scientists to spend time attempting to get a working model with the least amount of data possible and then iterating from there. When more data becomes available, the data science team’s focus shifts to cleaning the data, converting it to the required format, and conducting exploratory data analysis.



Let’s find out how data collection and AI modeling gets incorporated into an organization.

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Data Gathering and AI Modeling

Set up cloud accounts and extract data

Have a generalist team

Collect only the required data