What is the AI Life Cycle?

by | Last updated Mar 31, 2024 | Artificial Intelligence, Life Cycle


In the rapidly evolving world of artificial intelligence (AI), project management can be as complex as the technology itself. A staggering number of AI projects fail, not due to a lack of technical prowess, but because of ineffective project management. Implementing a well-defined AI project life cycle can significantly improve the success rate of these endeavors, transforming raw data and innovative ideas into practical, efficient solutions.

As shown below, the 6 key phases of the AI life cycle are (1) Problem Definition, (2) Data Acquisition and Preparation; (3) Model Development; (4) Model Evaluation and Refinement; (5) Deployment; and (6) MLOps.

AI life cycle


Understanding the AI Life Cycle

Conceptually, one can think of an AI project life cycle as the sequential progression of tasks and decisions that drive the development and deployment of AI solutions.

Problem Definition

This is where the journey begins. It involves defining the problem to be solved or the opportunity to be explored using AI. It’s a crucial stage that sets the direction for the entire project. Having a clear, well-defined problem helps guide data collection, model development, and ultimately, the successful implementation of the solution. This is where the role of an AI product manager can be useful.

Data Acquisition and Preparation

After identifying the problem, the next step is to collect and prepare data. AI and machine learning algorithms need data to learn, so this stage involves gathering relevant data and preparing it for use. This preparation may involve cleaning the data, dealing with missing values, or transforming the data into a format suitable for the chosen AI models. While the least glamorous, this can be the most time-consuming phase of the AI life cycle.

Model Development and Training

This phase involves developing the AI model that will solve the defined problem and training it with the prepared data. This stage is iterative, often involving multiple rounds of model development and refinement based on the model’s performance during training.

Model Evaluation and Refinement

Once the model has been trained, it must be evaluated to see how well it performs. This involves testing the model on unseen data and analyzing its predictions. If the model’s performance is not satisfactory, it’s refined and tweaked. This could mean adjusting the model’s parameters, changing the model’s architecture, or even returning to the data acquisition phase to gather additional data.


Once the model is performing satisfactorily, it is deployed to a production environment where it can start solving real-world problems. Deployment might involve integrating the model with existing systems, creating an application or service that uses the model, or leveraging the insights via an offline context such as a report to management.

Machine Learning Operations

Most of the time, after deployment, the model will need to be maintained and updated. In this machine learning operations phase, the team monitors the model’s performance to ensure it’s still working as expected, updating the model with new data, or refining the model based on feedback from its users.

Furthermore, teams often need to go back to a previous phase (ex. Going from model evaluation back to model development). This is to be expected and should be considered a normal part of the AI life cycle (and not an issue with the AI development team).

Importance of Each Stage in the AI Project Life Cycle

Each stage in the AI project life cycle serves a vital role. The problem definition phase establishes the project’s direction. The data acquisition and preparation phase creates the foundation for the AI solution. The model development and training phase turns this foundation into a functional tool. Then, the model evaluation and refinement phase ensures that the tool/model meets the expected standards. Finally, deployment brings the AI solution to its intended users, and maintenance keeps it running smoothly over time.

In addition, AI projects often need to adapt to changes quickly, whether these are changes in the project’s requirements, unexpected issues with the data, or new developments in AI technology. Building this adaptability into the project life cycle can be difficult but is crucial for long-term project success. This is where the use of an agile framework can help.

Benefits of Implementing a Robust AI Project Life Cycle

Employing a structured AI project life cycle has numerous benefits:

  • Increased Success Rate: A robust project life cycle helps ensure that each necessary step in the development of an AI solution is followed, greatly increasing the likelihood of project success.
  • Risk Reduction: By flagging potential issues early in the process, a well-structured project life cycle helps to mitigate risks. For example, during the problem definition phase, if the problem isn’t defined clearly, the project may lose direction. Identifying this risk early on allows teams to refocus and avoid costly, time-consuming revisions later on.
  • Improved Efficiency and Productivity: A structured project life cycle streamlines the workflow, ensuring that everyone on the team understands their roles and responsibilities at each stage. This clarity can significantly improve efficiency and productivity, reducing the time to deployment.
  • Enhanced Quality of AI Solution: By enforcing thoroughness and rigor at each stage, a well-defined project life cycle enhances the quality of the final AI solution. Rigorous evaluation and refinement ensure the AI solution performs as expected, while regular maintenance and updates keep it running smoothly over time.
  • Enhanced Resource Allocation: AI projects require significant resources, including time, human expertise, and computational power. Identifying and balancing these resources across each phase of the life cycle can be challenging, but being explicit about resource allocation across the life cycle can help the team appropriately resource the project.

In short, a well-defined AI life cycle can help teams plan their AI projects more effectively, maximizing their chances of success while minimizing potential hurdles.

An Example AI Project Life Cycle

Let’s explore the simple example of using the AI project life cycle in the development of AI-based recommendation system (specifically Amazon’s system recommending what to purchase):

  1. Problem Definition: The primary problem is clearly defined – to improve the accuracy of product recommendations and thereby enhance the shopping experience for users while driving increased sales.
  2. Data Acquisition and Preparation: Amazon collects vast amounts of user data, including browsing history, purchase history, and ratings. These data points can be identified as the critical information needed and collected / prepared for the model development phase.
  3. Model Development and Training: Amazon uses many machine learning models, such as collaborative filtering, to create their recommendation system. Their models are trained with the prepared data to predict a customer’s interests based on similarities with other customers.
  4. Model Evaluation and Refinement: The model is tested extensively, and its predictions are compared to actual customer behavior to evaluate its accuracy. Based on these tests, the model is continually refined and improved to increase the precision of its recommendations.
  5. Deployment: Once the recommendation model meets the performance benchmark, it is deployed on Amazon’s platform. The model now operates in real-time, suggesting products to users based on their browsing and purchasing behavior.
  6. Machine Learning Operations: Post-deployment, the model is continually monitored and updated. As user behavior and preferences evolve over time, the model is retrained and updated to ensure its recommendations remain relevant and accurate.

This is an example of the AI project life cycle in action, showcasing how each stage plays an important role in delivering a successful AI solution.  This AI life cycle works equally well for building/refining generative AI models…

Iterating through the AI Life Cycle

It is important to note that the AI life cycle should be thought of as an iterative process that incrementally delivers a better solution. In other words, each of the life cycle phases is typically revisited many times throughout an AI project.

In the context of an AI project life cycle, an MVP (Minimal Viable Product) is a simplified version of the AI solution that is developed as quickly as possible to validate the underlying concept. It includes just enough features to be usable by early customers who can provide feedback for future development. For example, in the model development and training phase, rather than training the AI model on the entire data set, an MVP might be trained on a subset of the data to speed up the development process. This allows the team to quickly validate whether their approach is viable before investing more resources.

By leveraging an MVP and gathering user feedback, teams can identify any issues or areas for improvement early in the development process, making it easier to make changes and enhancements before the full solution is rolled out.

Key Take-aways

Using a well-defined AI project life cycle should not be optional—it should be an integral part of successful AI development. Embracing a life cycle approach can significantly improve the efficiency, productivity, and overall success of AI projects, making it an essential consideration for any team venturing into the world of AI.

For more information on AI project management, explore our post on 6 Concepts to Help Lead an AI Team.


Explore Related Content

Finally...a field guide for managing data science projects!

Data science is unique. It's time to start managing it as such.

Get the jumpstart guide to manage your next project better.

Plus get monthly tips in data science project management.

You have Successfully Subscribed!

Share This