Editing Machine learning (section)

=== Training models ===
Typically, machine learning models require a high quantity of reliable data to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative [[Sample (statistics)|sample]] of data. Data from the training set can be as varied as a [[corpus of text]], a collection of images, [[sensor]] data, and data collected from individual users of a service. [[Overfitting]] is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Biased models may result in detrimental outcomes, thereby furthering the negative impacts on society or objectives. [[Algorithmic bias]] is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably, becoming integrated within machine learning engineering teams.

==== Federated learning ====
{{Main|Federated learning}}
Federated learning is an adapted form of [[distributed artificial intelligence]] to training machine learning models that decentralises the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralised server. This also increases efficiency by decentralising the training process to many devices. For example, [[Gboard]] uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to [[Google]].<ref>{{Cite web|url=http://ai.googleblog.com/2017/04/federated-learning-collaborative.html|title=Federated Learning: Collaborative Machine Learning without Centralized Training Data|website=Google AI Blog|date=6 April 2017 |language=en|access-date=8 June 2019|archive-date=7 June 2019|archive-url=https://web.archive.org/web/20190607054623/https://ai.googleblog.com/2017/04/federated-learning-collaborative.html|url-status=live}}</ref>