Favorite Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to find insights and relationships like people, places, sentiments, and topics in unstructured text. You can now use Amazon Comprehend ML capabilities to detect and redact personally identifiable information (PII) in customer emails, support tickets,
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Shared by AWS Machine Learning September 17, 2020
Favorite AWS is excited to announce the winner of the second AWS DeepComposer Chartbusters challenge, Lena Taupier. AWS DeepComposer gives developers a creative way to get started with machine learning (ML). In June, we launched the Chartbusters challenge, a global competition where developers use AWS DeepComposer to create original compositions
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Shared by AWS Machine Learning September 16, 2020
Favorite In an effort to drive customer service improvements, many companies record the phone conversations between their customers and call center representatives. These call recordings are typically stored as audio files and processed to uncover insights such as customer sentiment, product or service issues, and agent effectiveness. To provide an
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Shared by AWS Machine Learning September 16, 2020
Favorite We all suffer from bandwidth issues in KM – generally due to the deluge of information. Here’s a good principle from the military for dealing with these issues. Information overload by SparkCBC on Flickr The phrase – “Smart push, warrior pull” (described here). is a very useful military principle for
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Shared by Nick Milton September 16, 2020
Favorite Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. As
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Shared by AWS Machine Learning September 15, 2020
Favorite Amazon Personalize is a machine learning (ML) service that enables you to personalize your website, app, ads, emails, and more with private, custom ML models that you can create with no prior ML experience. We’re excited to announce the general availability of Amazon Personalize in the EU (Frankfurt) Region.
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Shared by AWS Machine Learning September 15, 2020
Favorite I wrote a blog post yesterday on 4 types of KM plan, and (too late) realised that there were more than 4. Here are another four types. Yesterday’s blog post mentioned the following 4 types of plan, which are all at a fairly high level of granularity. These are:
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Shared by Nick Milton September 15, 2020
Favorite The new Amazon SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio notebooks via a new CLI. The new CLI eliminates the need to manually set up and connect to Docker build environments for building container images in
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Shared by AWS Machine Learning September 14, 2020
Favorite Knowledge Management plans exist at many scales. Here are 4 of them. KM planning session Implementing KM is a project, and a prject needs a plan. However KM can be implemented at many scales, and many variants of KM plan may be needed. In this post we describe 4
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Shared by Nick Milton September 14, 2020
Favorite Amazon SageMaker is a fully managed service that allows you to build, train, deploy, and monitor machine learning (ML) models. Its modular design allows you to pick and choose the features that suit your use cases at different stages of the ML lifecycle. Amazon SageMaker offers capabilities that abstract
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Shared by AWS Machine Learning September 11, 2020
Shared by AWS Machine Learning September 17, 2020
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