Data challenges are one of the biggest blockers to Machine Learning (ML). So what are those challenges, and how can we overcome them?
The data that powers Machine Learning (ML) is as important as the models themselves. ML algorithms learn from data; finding relationships, making decisions from the training data they’re given. The better the training data is, the better the model performs. However, data requirements for ML are very different to those for traditional business operations, and doing ML well requires that you understand the difference. .
Join us on Friday 27 August at 12.30pm when DiUS Machine Learning Leads, Nigel Hooke and Nabi Rezvani, will outline how to measure data quality and readiness for ML. They will also outline some common data readiness challenges and how to build the necessary data infrastructure, including:
