Estimating the total investment in an AI or machine learning solution is not too different from typical technical project estimation. There are some unique elements to consider as your organization is getting started, though.
The AI and Data Science Build Process: Unlike typical software engineering projects, which often follow a linear process, the AI and data science build process is highly iterative. Data scientists may experiment with various models, inputs, and technical approaches, constantly iterating to enhance subsequent models. This lack of predictability adds complexity to estimating both time and cost, making it a critical consideration.
Training the Models: A Unique Cost Aspect: Training AI models can be an expensive and computationally intensive process, depending on the model's size and complexity. The costs behind training models like ChatGPT, which experts estimate to be in the millions of dollars, serve as a stark example. Though not all models will require such substantial investment, the cost of training should be a vital consideration in your own projects.
Productionizing and Making Inferences: Integrating AI and machine learning tools into software systems or automated workflows is another significant investment. This includes not only the cost of integration but also the sometimes computationally intensive and costly process of making inferences with the models. Aligning the integration with organizational goals and budget constraints is essential.
Maintaining Systems: An Ongoing Commitment: AI and machine learning models require ongoing maintenance, as their performance may degrade over time due to changes in the world around them. The need to retrain and occasionally rearchitect models to maintain optimal performance adds another layer of complexity and cost to the investment.
Collaborating with Data Science Counterparts: Given these unique aspects, organizations must work closely with data science counterparts to understand the estimated costs across the entire lifecycle of AI solutions. Such collaboration ensures a more realistic and comprehensive understanding of the total investment.