Got Data? The Importance of High-Quality Data for Building Effective Machine Learning-Based Solutions

Watch this webinar for key insights on how to collect data for machine learning, including pros and cons and trade offs that come with different approaches.

When it comes to annotating data for academic purposes, there are specific industry standards that are commonly used.  However, when it comes to the commercial sector, building a solution that relies on machine learning requires different data annotation standards.  To build a strong solution that can understand and mimic humans, high-quality, human-annotated training data is key.

Interested in learning more? Watch our free on-demand webinar, part of AI Trends’ webinar series.

In this webinar, James Lyle, Director of Custom Linguistic Solutions, talks about how to collect data for machine learning including:

  • The pros and cons of licensable public data vs. building your own datasets
  • Choices and tradeoffs in the level of effort you invest in acquiring and labeling data
  • Why curated crowds yield higher quality data for your machine learning

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