Regardless of the changes, the estimation should be made after identifying the type of project being executed. The first step you would undertake in the cost estimation is to define the scope of the ML project. Begin this with the basics and ask questions such as: What data is needed for training? What is the expected output? How will the quality of training models be measured? Will the output accuracy be acceptable? Which systems will be integrated into the ML system? Finding answers to these questions will give you a scope of the entire ML project.
Separate ML tasks from those that are not connected to them to allow accurate cost estimation. Doing this will eliminate other tasks that are not directly related to the machine learning project, but which are part of the system. Do so by identifying all aspects of the project that does not need AI-related skills. To achieve this, ask yourself questions such as: How will the data be fed into the ML models? How will the end-user interact with the system output? Is the data format ready to use? What other external sources need to be used? After answering all these questions, you can easily estimate the costs with little error.
After the separation of tasks, you need to choose the development approach which may have a bearing on the cost of your project. Doing so is simple, mainly if you are a professional in this field or have worked on such projects in the past. You will need to estimate the cost of data since this is often the most important asset in any machine learning project. Machine learning projects are data-hungry and cannot function as they could without the right data. A study by Dimension Research indicates that 96% of organizations face issues related to the quality and quantity of training data.
There is also the cost of research. Any serious ML project entails extensive research before production. Look into the members of the project research team. According to Dimensional Research, an average team has about 5 members. Take into consideration the number of outsourced team members, which can be about 3 members. Additionally, estimate the cost of production, which includes the cost of cloud compute or data storage, integration costs (API development, documentation), and the cost of maintenance. There is also some other infrastructure that is necessary for the project to operate effectively. Maybe the lowest cost you will incur here is the one associated with cloud computing. Lastly, there is the opportunity cost.
In conclusion, ML projects are hard to estimate. However, having the right analysis of all the non-AI costs can clear a lot of things and can ease the process. Machine Learning projects vary depending on the implementation approach. Each approach to implementation has different costs, time, and cost probability. The best way to get around these challenges is to estimate the cost of every aspect of the project, one step at a time.