Model serving made easier with Deep Java Library and AWS Lambda

Favorite Developing and deploying a deep learning model involves many steps: gathering and cleansing data, designing the model, fine-tuning model parameters, evaluating the results, and going through it again until a desirable result is achieved. Then comes the final step: deploying the model. AWS Lambda is one of the most cost effective service

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Shared by AWS Machine Learning January 25, 2021

Building your own brand detection and visibility using Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels – Part 1: End-to-end solution

Favorite According to Gartner, 58% of marketing leaders believe brand is a critical driver of buyer behavior for prospects, and 65% believe it’s a critical driver of buyer behavior for existing customers. Companies spend huge amounts of money on advertisement to raise brand visibility and awareness. In fact, as per

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Shared by AWS Machine Learning January 25, 2021

Why incrementalism doesn’t work for KM change

Favorite Incrementalism will not work as a way to introduce Knowledge Management. KM is a mindshift – a giant leap – not a series of small steps. One giant leap by Vivobarefoot on Flickr Incrementalism is a method of working or changing by using many small incremental changes instead of

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Shared by Nick Milton January 22, 2021

Redacting PII from application log output with Amazon Comprehend

Favorite Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to find insights and relationships in text. The service can extract people, places, sentiments, and topics in unstructured data. You can now use Amazon Comprehend ML capabilities to detect and redact personally identifiable information (PII)

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Shared by AWS Machine Learning January 20, 2021