TDWI Checklist Report: Five Data Engineering Requirements for Enabling Machine Learning

Machine learning can provide competitive advantage to those organizations that use it. As data volume and diversity grows, organizations will need to revisit their data management strategy to support machine learning.

Making the jump from test and training environments to full production environments requires a smart data pipeline strategy. This includes ensuring that the right tools and processes are in place so that all the data used in model building is accessible, clean, understood and governed. It also means that the data environment needs to support operationalizing machine learning models against new and big data, which will necessitate keeping data current and involve real-time processing and automation.

Read on to learn about the challenges facing organizations that want to take advantage of machine learning and best practices for data engineering and management to support machine learning.

Checklist: Data engineering for machine learning

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