How to Operationalize Machine Learning Projects

Machine Learning (ML) can empower a business and optimize its performance. However, according to Gartner, 85% of data science and machine learning projects fail.

These projects aren’t delivering value because companies lack technical skills and have recurring issues with data infrastructure, deployment, and operationalization.  

This video provides you instructions on how to operationalize your ML projects and use them in production by lowering skill requirements and building trust.

Presentation agenda:

1. Why ML is seldom deployed to production
2. How to democratize technology and lower skill requirements to operationalize ML
3. How to build trust in ML decisions