This step is the most time-consuming, with ML engineers spending around 80% of the AI model development time in this stage. A significant amount of time is spent cleaning the data and transforming it into the required format.

Things to consider include:

• Trans...

Ask questions, such as.

• What data is needed to solve the business problem?
• What quantity of data is required?
• Do you have enough data to build a model?
• Do you need more data to extend the existing data?
• Ho...

While the model is trained and tuned using the training and validation data set, the model will behave differently when used in the real world, which is fine.

The main objective is to minimise the change in model behaviour when it is deployed. Three data sets are used when ...

Analyse if the KPIs and the business objective of the model are achieved. If the parameters are not met, consider changing the model or improving the quality and quantity of the data.

Before deployment:

• Ensure to measure and monitor the model performance continuously...

AI model development involves multiple stages that interconnect to each other.

1. Identify the business problem. Instead of asking how to improve your artificial intelligence, ask how to improve your business.
2. Identify and co...

At this step, all the requirements have been collected for the solution modelling to proceed.

ML engineers will define the features of the model, taking the following into account:

• Use the same features for training and testing the model to avoid inaccurate results.