Human-in-the-Loop for Machine Learning

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Businesses can benefit from both the efficiency of machine learning (ML) as well as the quality of human judgement. An increasing part of the ML solution is human-in-the-loop (HITL), where human feedback is provided to evaluate the output of ML algorithms, i.e., to determine its validity and help refine the result. An example is image classification, where the task might be too ambiguous for a purely mechanical solution and too vast for even a large team of human experts. In this session, learn how to effectively incorporate human-in-the-loop in your ML projects to achieve higher accuracy and better results with Amazon Mechanical Turk (Mechanical Turk). Learn More –

Catch up on the excitement of re:Invent 2018 with the AWS launchpad featuring launch announcements, demos of newly launched technology, interviews with expert guests and live Q&A. AWS re:Invent is a tech education conference for the global cloud computing community hosted by Amazon Web Services. See all recordings of the AWS Launchpad at re:Invent here: and learn more about AWS live streaming here:


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