By using website you agree to our use of cookies as described in our cookie policy. Learn More


Ten Mistakes to Avoid in the Machine Learning Life Cycle

Ten Mistakes to Avoid in the Machine Learning Life Cycle

February 24, 2021

Machine learning—where systems examine data to identify patterns with minimal human intervention—is becoming part of the analytics fabric of many organizations as its competitive value becomes understood.

Although many companies are excited about machine learning, they often overlook some key success factors. To succeed in machine learning, enterprises must embrace the full machine learning life cycle in a unified way—from data management and governance to data engineering to building the model and putting it into production while ensuring that the organizational culture embraces predictive applications.

Some of the biggest challenges with machine learning have to do with everything around the actual machine learning workflows, including preparing and automating data pipelines, creating explainable predictions, managing models, and building trust, so it is important to not make the interrelated ten mistakes described here.

Your e-mail address is used to communicate with you about your registration, related products and services, and offers from select vendors. Refer to our Privacy Policy for additional information.

TDWI Membership

Get immediate access to training discounts, video library, research, and more.

Find the right level of Membership for you.