New eBook! TDWI Checklist Report: Five Data Engineering Requirements for Enabling Machine Learning
Syncsort has released a new eBook, “Five Data Engineering Requirements for Enabling Machine Learning,” which is now available to download. Machine learning can provide a 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.
Our TDWI checklist report offers 5 points for enabling machine learning:
- Support rich data sources
- Ensure data quality
- Engineer the right features for the problem
- Keep the data current
- Govern the data
Learn about some of the challenges facing organizations that want to take advantage of machine learning and best practices for data engineering and management to support machine learning.