5 Strategies for Generating Machine Learning Training Data

Have you run into issues acquiring the right type of data for your machine learning (ML) projects?

You’re not alone. Many teams do. And data is one of the key sticking points in starting AI initiatives at companies. In fact, according to IBM’s CEO, Arvind Krishna, data-related challenges are the top reason IBM clients have halted or canceled AI projects.

Often what happens in practice is that the relevant ML training data is either not collected, or collected but the data lacks the required labels for training a model. It could also be that the existing volume of data is insufficient for ML model development.

As I’ve discussed in one of my previous data articles, such issues result in delays, project cancellation, biased predictions, and an overall lack of trust in AI initiatives. Bottom line: having the right data, in the right volume is critical for any ML project.

But, what if your company does not have a solid big data strategy, or you’re just getting started with data collection? How can you safely start machine learning projects for your automation tasks?

In this article, we’ll explore five strategies for obtaining high-quality machine learning training data for your projects, even if you’re new to AI or your data strategy is still in…