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Human in the loop Workflow Automation
A talk given by Tina Huang from Transposit at the 2019 Platform Summit in Stockholm. Manual workflows are productivity killers. Automation has become our North Star. But many workflows can only be partially automated; they may benefit from or require human intervention. APIs give us the levers we need to build great automation. Applications provide the interfaces that pull humans into the loop at critical junctures. Composition is how we turn those APIs into apps that integrate disparate applications, simplify workflows, notify people, and respond to interactions. In this talk, we’ll demonstrate how we’ve built apps that allow for human-in-the-loop automation and show you how using an API composition platform like Transposit can help you efficiently build apps and bots that automate the tedium and let humans add maximal value.
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dabl: Automatic Machine Learning with a Human in the Loop |SciPy 2020| Andreas Mueller
In many real-world applications, data quality and curation and domain knowledge play a much larger role in building successful models than coming up with complex processing techniques and tweaking hyper-parameters. Therefore, a machine learning toolbox should enable users to understand both data and model, and not burden the practitioner with picking preprocessing steps and hyperparameters.
The dabl library is a first step in this direction. It provides automatic visualization routines and model inspection capabilities while automating away model selection. This talk will introduce the dabl library and show how to use it to quickly create supervised models and identify modeling and data quality issues.
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Easily Implement Human in the Loop into Your Machine Learning Predictions with Amazon A2I
Companies have millions of documents to process along with various types of documents. Often times, these documents are hard to read or have specific data points which are required to complete the business process. Using Amazon Augmented AI, you can now implement human reviews to review your machine learning predictions from Amazon Textract, Amazon Rekognition, Amazon SageMaker and many AWS AI/ ML services. In this tech talk, we walk through how to implement human reviews as well as showcase a use case by DealNet Capital on how they were able to reduce review time by 80% implementing Amazon A2I. Learning Objectives: Learn how to implement human reviews, Understand how Amazon A2I can work with other machine learning services, Learn how DealNet used Amazon Textract and Amazon A2I to process loan applications. To learn more about the services featured in this talk, please visit: http://aws.amazon.com/augmented-ai
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