Sentence examples for deploy machine learning from inspiring English sources

Perhaps the best pure-play example of the power of learning effects is Tesla, which began as an electric car company but was able to deploy machine learning to extraordinary effect across its fleet to become the category leader in autonomous driving.

The Experimentation Service is all about helping developers quickly train and deploy machine learning experiments.

Deploy machine learning and data analysis technologies on your publishing platforms to dynamically adjust metadata and tagging to improve SEO and content discovery.

As AWS CEO Andy Jassy put it while introducing the new service on stage at re:invent, "Amazon SageMaker, an easy way to train, deploy machine learning models for every day developers".

There is a divide between the tech stacks used to train and deploy machine learning models in the cloud and the ones used to build applications for edge devices, like smartphones and IoT.

Founded in 2013 by three engineering graduates from Imperial College — Tim Sadler, Tom Adams and Ed Bishop — Tessian is deploying machine learning to improve email security.

Tessian (formerly called CheckRecipient), the London-based startup deploying machine learning to improve email security, has raised $13 million in Series A funding.

The work of [23] also deployed machine learning for the advertisement classification problem, by training a supervised learning classifier on labeled data (based on phone numbers of known traffickers) provided by a victim advocacy group.

Yet, back in February, Facebook announced new "stronger" anti-discriminatory ad polices, saying it was deploying machine learning tech tools to help it identify ads in the categories of concern.

They deployed machine learning algorithms and statistical models, but ultimately found that there just wasn't enough data: Their processes successfully identified reviews relating to recalled products, but couldn't predict those recalls with any accuracy.

PS studies must be conducted on multiple scales derived from concerted observations in multiple animal models and human cohorts performed in an interactive and iterative manner and deploying machine learning for data synthesis, identification and validation of the best non-invasive detection and follow-up biomarkers, a prerequisite for designing effective therapeutic interventions.