High quality data is key for building useful machine learning models. Machine learning models learn their behaviour from data. So, finding the right data is a big part of the work to build machine learning into your products.
Machine learning models learn their behaviour from data. So, finding the right data is a big part of the work to build machine learning into your products.
Exactly how much data you need depends on what you’re doing and your starting point. There are techniques like transfer learning to reduce the amount of data you need. Or, for some tasks, pre-trained models are available. Still, if you want to build something more than a proof-of-concept, you’ll eventually need data of your own to do so.
That data has to be representative of the machine learning task, and its collection is one of the places where bias creeps in. Building a dataset that’s balanced on multiple dimensions requires care and attention. Data for training a speech recognition system has to represent aspects like different noisy environments, multiple speakers, accents, microphones, topics of conversation, styles of conversation, and more. Some of these aspects, like background noise, affect most users equally. But some aspects, like accent, have an outsized impact on particular groups of users. Sometimes, though, bias is built deeper into the data than in the composition of the dataset. Text scraped from the web, for example, results in a dataset that embeds many of society’s stereotypes because those are present in text from the web and can’t be scrubbed.
For building successful machine learning models, sourcing data is a critical part of designing and building the overall system. As well as finding data that’s effective for the task, you have to weigh up cost, time to market and data handling processes that have to be put into place. Each source of data has its own pros and cons, and ultimately you might use some combination of data from the sources below.
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