A survey revealing that 85% of AI projects fail to deliver on their promises to businesses highlights the significance of AI project management, or more specifically, managing AI initiatives.

The management of an AI project is distinct from the management of a regular software development project because AI projects are unique. This essay discusses 6 essential aspects that can help you better your management of AI projects in order to aid in the process.

1: To manage AI projects successfully, be aware of how AI insights will be applied.

Even while it may seem clear to understand the issue at hand, the data that could be helpful to construct a predictive model, and how that model would be used inside the business, teams frequently struggle in this area. In fact, a lot of teams immediately start talking about utilising machine learning services to create a certain model with a particular set of traits.

An essential consideration that should not be ignored is taking the time to step back and comprehend the actual organisational or commercial difficulty that could be resolved with the help of an AI or labeling in machine learning solution. The team will be able to properly brainstorm and prioritise the entire spectrum of tasks in this context is provided (e.g., what data might be useful, what to predict, and how to analyse if an AI predictive model is useful).

2: Be familiar with the project’s conceptual design for AI.

It is useful to think of the system as three major, interdependent parts while developing an AI solution. There is a front-end component (such as a user interface) and a back-end component, just like with software systems (e.g., store and access data). However, ML is also a part of AI systems (e.g., generate and use predictive models).

For instance, a recommendation system, like those used by Amazon or Netflix, comprises a front-end component that displays the user interface and a back-end component that keeps track of various users (e.g., movies that you might want to watch). The movie suggestions are produced by the ML component.

We might only display the most well-liked episodes or prior movies the user has seen for a “regular” software system. The front-end user interface would receive this kind of data from the back-end. However, machine learning algorithms are crucial for predictions (such as what the individual would wish to watch)!

3. Know the project management and execution life cycle you’ll employ for AI projects.

Fewer resources are accessible to assist you to comprehend the life cycle needed to design a machine learning predictive model, despite the fact that numerous publications explain the SDLC (software development life cycle). At a high level, the group will have to repeat the following procedures:

• Understand the business problem and the that might be data available
  • Clean and “munge” the data
  • Use Machine Learning to build a predictive model
  • Deploy the model
  • Observe and analyze how the model performs

4. Be able to coordinate between and among the teams working on your IT and AI projects.

Although knowing how to develop a predictive model is helpful, there needs to be a procedure to coordinate efforts both within an AI/Data science endeavor and across the team. The Scrum and Data-Driven Scrum frameworks both outline how the team might operate in an agile manner, with brief work iterations and meetings after each iteration to discuss lessons learned, suggest next steps, and prioritise potential future work.

5. Understanding when and how to grow the solution

It is usually advisable to begin small and then build up the solution over time. The data science/ML team shouldn’t be “throwing the code over the wall” to an IT DevOps team in order to achieve this gradual scalability. The DevOps team must collaborate with the

data science team.It is important to consider how the group will provide “machine learning operational support” as a whole at the beginning of the project and to make adjustments as the project grows in usage.

6. Active AI project management can be used to investigate potential model bias.

Model bias can result from using a training dataset that is not completely representative of the population where the model will be employed. This bias could, for instance, result from not receiving the complete spectrum of applicants. The team should consider where bias might be introduced and how to limit any potential bias, even though it is challenging to completely eradicate bias.

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