It’s well-known that the quantity and quality of training data for any given artificial intelligence (AI) project are two of the most crucial factors for that project’s success. Insufficient or poor training data can result in an unreliable system that reaches the wrong conclusions, makes poor decisions, can’t handle real-world variation, and introduces or perpetuates bias, among other problems. It’s also expensive! Without a well-defined strategy for collecting and structuring the data you need to train, test, and tune your AI systems, you run the risk of delayed projects, not being able to scale appropriately, and ultimately, competitors outpacing you.
In our white paper, How to Develop a Training Data Strategy for Machine Learning, we discuss how to create a solid machine learning training data strategy, including budgeting, options for data sourcing, how to ensure data quality and security, and how outsourcing the collection and labeling of training data can help scale your AI initiatives.
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, many 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 your models. That’s why choosing the right data annotation technology is a key piece of your training data strategy. Our 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.
What does the white paper cover?
Download this white paper to learn best practices for training data for your machine learning program. Our white paper covers important elements for a successful machine learning training data strategy including:
- Budgeting. Learn about the investment required to get your initiative off the ground, and maintain it.
- 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.
- Annotation resources. Know what important considerations factor into the decision to either outsource your data annotation or source it internally.
Appen, through its recent acquisition of Figure Eight, provides the industry’s leading solution for machine learning data collection, annotation, and crowd workforce management. Look no further for tightly integrated services that provide and manage a pipeline that meets your training data needs.
Ready to improve the quality of your machine learning initiative?
At Appen, we’ve helped leaders in machine learning and artificial intelligence scale their programs from proof of concept to production. Contact us to learn more.