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White Papers:

How to Develop a Training Data Strategy for Machine Learning

Why you need to establish a robust training data pipeline

Since better outcomes are more likely when your training data is as intricate or nuanced as possible, most machine learning initiatives require large volumes of high-quality training data, fast and at scale. To achieve this, you need to build a data pipeline that delivers sufficient volume at the speed needed to refresh their models. That’s why choosing the right data annotation technology is a key piece of your training data strategy. Our latest white paper highlights several important considerations to keep in mind when making this decision:

  • The tools must handle the appropriate data types for your initiative.
  • The platform should allow for experimentation.
  • The technology should be able to both manage an individual annotator’s quality and throughput for data labeling tasks, as well as overall project quality and efficiency metrics.

Download this white paper to learn training data best practices, including:

  • Budgeting. Learn about the investment required to get your initiative off the ground, maintain it, and evolve the features and functionality.
  • Data sourcing. The type of data you’ll need depends on the kind of solution you’re building. Learn what data types are appropriate for your project and how to acquire it.
  • Data labeling. Learn about popular strategies for annotating data accurately and expeditiously.
  • Quality and security. Gain a deeper understanding of how these two critical aspects of any data training project can affect business outcomes.
  • Outsource or internal. Know what important considerations factor into the decision to either outsource your data annotation or source it internally.

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