Building Machine Learning Pipelines: Common Pitfalls - neptune.ai

In recent years, there have been rapid advancements in Machine Learning and this has led to many companies and startups delving into the field without understanding the pitfalls. Common examples are the pitfalls involved when building ML pipelines. Machine Learning pipelines are complex and there are several ways they can fail or be misused. Stakeholders involved in ML projects need to understand how Machine Learning pipelines can fail, possible pitfalls, and how to avoid such pitfalls. There are several pitfalls you should be aware of when building machine learning pipelines. The most common pitfall is the black-box problem -- where the pipeline is too complex to understand.