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.
March 2 - Mistakes to Avoid When Building and Deploying Machine Learning Programs: Part 1
May 6 - Mistakes to Avoid When Building and Deploying Machine Learning Programs: Part 2
July 8 - Mistakes to Avoid When Building and Deploying Machine Learning Programs: Part 3
Sponsored by Cloudera
Individual, Student, and Team memberships available.