The art of choosing the right machine learning project. How to rank and evaluate machine learning projects from an engineering perspective
Machine Learning projects are known to fail frequently, according to Gartner 85% of all AI projects fail and even 96% fight with problems. Sure, when it comes to new technologies a high degree is normal, but these numbers are alarming. Typically, you read a lot about data quality, exaggerated expectations and wrong or non-existent goals. However, some of these issues can be avoided by assessing the projects in more detail before selecting a project for a Data Science/Machine Learning team. I would like to highlight some aspects from the perspective of an AI developer.
This is supposed to be about applied machine learning projects; therefore, we first must define what this means and create a definition. To do this, it helps that we divide the misty term Data Science into Analytics, Statistics and Machine Learning, like others did before. Analytics here is supposed to describe the exploration focus, i.e. the illustration and exploration of data. Typically, questions arise here that need to be answered. Answering these questions is the task of statistics. Mostly these answers are complex and allow to make decisions based on them. Machine Learning comes into play when it comes to automating these or resulting decisions. This logical structure alone makes clear that there must be many more questions in the field of analytics and statistics than machine learning projects can arise from them.
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AI, Machine learning, as its title defines, is involved as a process to make the machine operate a task automatically to know more join CETPA