10. Not Curating And Balancing Training Data

Organizations can avoid a key mistake in AI model training by carefully curating and balancing their training data. Biased data can cause inaccurate or unfair predictions, so organizations should regularly evaluate their data for biases and mitigate them through techniques such as oversampling, data augmentation or bias removal. This leads to more ethical and effective AI models. – Imane Adel, Paymob

11. Neglecting To Define Objectives

Organizations frequently neglect to adequately define and validate their objectives when training AI models. Without specific goals, it can be challenging to judge an AI model’s effectiveness, which can result in subpar performance or unforeseen effects. – Neelima MangalSpectrum North

12. Not Including The Customer’s Voice

The biggest mistake is not including the voice of the customer. If you include a customer in AI training sessions, it will bring guaranteed insights that no one in your organization possesses. As an added benefit, including them can help make an important customer feel even more important. – Rhonda Dibachi, HeyScottie.com

13. Not Sanitizing The Data

Organizations frequently overlook properly curating and sanitizing the data used for training AI models, which is a critical error. Poor data quality can result in AI models that are biased, imprecise and unreliable. Negative effects, including poor judgments, missed chances and reputation loss, may occur from this. – David Bitton, DoorLoop

14. Not Ensuring Data Represents Good And Bad Behaviors

If AI models are trained on the wrong data, the predictions will also bake in those incorrect behaviors. When dealing with AI and ML applications for security and avoiding data breaches, ensuring that the data represents good and bad behaviors is essential. Data quality during feature mining—ensuring the right labels are in place for supervised learning—is important for organizations training AI models. – Supreeth Rao, Theom, Inc

15. Not Accounting For Data Shift And Semantic Shift

As an organization scales and moves into new domains, countries and business lines, the data that its models were trained on starts to shift based on the data that its users are currently inputting. The training of AI models needs to be a constant process, and a lot of attention needs to be paid to acquiring quality, representative data. – Isaac Heller, Trullion