A Technology Broker and Management Company Specializing in KM Systems
Favorite “Knowledge management is a powerful force multiplier, that creates shared understanding, and informs and enhances the decision making process… It is not a function performed by a few people in HQ; it’s part of everyone’s job” These are two of the messages from this introductory video from TRADOC, the
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Shared by Nick Milton August 18, 2021
Favorite As customers continue to come up with new use-cases for machine learning, data gravity is as important as ever. Where latency and network connectivity is not an issue, generating data in one location (such as a manufacturing facility) and sending it to the cloud for inference is acceptable for
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Shared by AWS Machine Learning August 17, 2021
Favorite Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models built on different frameworks. SageMaker real-time inference endpoints are fully managed and can serve predictions in real time with low latency. This post introduces
Read More Shared by AWS Machine Learning August 17, 2021
Favorite What is your organisation’s learning rate? What could KM increase this to? And what’s the value of the difference? Over time, with any product or process, the costs come down. The rate of decrease is known as the learning rate. One value proposition for Knowledge Management is to accelerate
Read More Shared by Nick Milton August 17, 2021
Favorite You can use AWS Snowball Edge devices in locations like cruise ships, oil rigs, and factory floors with limited to no network connectivity for a wide range of machine learning (ML) applications such as surveillance, facial recognition, and industrial inspection. However, given the remote and disconnected nature of these
Read More Shared by AWS Machine Learning August 12, 2021
Favorite In a machine learning (ML) journey, one crucial step before building any ML model is to transform your data and design features from your data so that your data can be machine-readable. This step is known as feature engineering. This can include one-hot encoding categorical variables, converting text values
Read More Shared by AWS Machine Learning August 12, 2021
Favorite This is a guest post by Ernesto DiMarino, who is Head of Enterprise Applications and Data at Cortica. Cortica is on a mission to revolutionize healthcare for children with autism and other neurodevelopmental differences. Cortica was founded to fix the fragmented journey families typically navigate while seeking diagnoses and
Read More Shared by AWS Machine Learning August 11, 2021
Favorite As more machine learning (ML) workloads go into production, many organizations must bring ML workloads to market quickly and increase productivity in the ML model development lifecycle. However, the ML model development lifecycle is significantly different from an application development lifecycle. This is due in part to the amount
Read More Shared by AWS Machine Learning August 11, 2021
Favorite Still the confusion remains between Information Management/Enterprise Content Management and Knowledge Management. Here are 5 points of difference and 1 point of overlap. In fact these pictures only have one point of similarlity. Much like KM and IM/ECM I have covered the difference between KM and IM many times
Read More Shared by Nick Milton August 10, 2021
Favorite Amazon Comprehend is a natural language processing (NLP) service that provides APIs to extract key phrases, contextual entities, events, sentiment from unstructured text, and more. Entities refer to things in your document such as people, places, organizations, credit card numbers, and so on. But what if you want to
Read More Shared by AWS Machine Learning August 10, 2021