Machine learning (ML), the most popular type of AI, provides enterprises with a much-needed tool to compete in today’s complex, fast-changing business environment. To make machine learning succeed at scale, data science teams must standardize and streamline the ML workflow – also known as MLOps – that spans data and feature engineering, model development, and model production.
This report examines how a cloud data platform enables teams to standardize and manage the ML lifecycle to help organizations achieve the scalability, reproducibility, and governance they need to succeed with machine learning.
Read this report to learn:
The team roles and stages of the machine learning lifecycle
How a cloud data platform helps streamline this lifecycle
Guiding principles to optimize MLOps on a cloud data platform