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Data Acquisition and Preparation
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Data Acquisition and Preparation

Enterprise machine learning is made possible by the large and growing amount of data now captured by the typical company. But simply having the data is not enough. Successfully scaling machine learning in the enterprise depends on an organization’s ability to effectively harness its data resources in an efficient and repeatable manner. There are estimates that this can take up to 80% of the time required for an ML project so this is a key competency for organizations to develop. The organizations that do this most successfully have processes and technology in place supporting:

  • Centralized data access;
  • Data Preparation
  • Data Cleansing;
  • Data Transformation;
  • Data Labeling;
  • Data Versioning;
  • Repeatable Data Pipelines;
  • Feature Extraction and Engineering;
  • Feature Store.
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