Today no less than 60% of companies are either exploring the possibilities of adopting artificial intelligence or trying to realize its potential to transform the way they do business. The problem is that a significant portion of them (one-third) struggle to produce substantial change with AI.
The lifecycle of an AI solution usually consists of problem definition, data collection, model building, model fine-tuning, and applying the solution to solve a specific problem. Various experts build the solution to solve business problems. Still, a problem solved by a data scientist does not automatically translate into a constant stream of actual value for the business. Once deployed to production, the AI solution cannot be left as-is. Like any other system, it requires continuous maintenance. However, any AI solution’s maintenance differs significantly from the maintenance of other systems (e.g., microservice-based applications). The performance of any AI solution can be affected by many factors, and if the maintenance work is not done, the solution will cause problems instead of solving them.
