A Technology Broker and Management Company Specializing in KM Systems
Favorite For many enterprises, critical business information is often stored as unstructured data scattered across multiple content repositories. It is challenging for organizations to make this information available to users when they need it. It is also difficult to do so securely so that relevant information is available to the
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Shared by AWS Machine Learning July 16, 2021
Favorite Anomaly detection is the process of identifying items, events, or occurrences that have different characteristics from the majority of the data. It has many applications in various fields, like fraud detection for credit cards, insurance, or healthcare; network intrusion detection for cybersecurity; KPI metrics monitoring for critical systems; and
Read More Shared by AWS Machine Learning July 14, 2021
Favorite There are 5 generic steps to go through when introducing a Knowledge management culture. These are as follows. Define the culture you want to develop. Don’t define it in woolly terms – “we want a knowledge sharing culture” – but define it in terms of the attitudes and behaviours
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Shared by Nick Milton July 14, 2021
Favorite Defect detection during manufacturing processes is a vital step to ensure product quality. The timely detection of faults or defects and taking appropriate actions are essential to reduce operational and quality-related costs. According to Aberdeen’s research, “Many organizations will have true quality-related costs as high as 15 to 20
Read More Shared by AWS Machine Learning July 13, 2021
Favorite This post outlines the best practices for provisioning Amazon SageMaker Studio for data science teams and provides reference architectures and AWS CloudFormation templates to help you get started. We use AWS Service Catalog to provision a Studio domain and users. The AWS Service Catalog allows you to provision these
Read More Shared by AWS Machine Learning July 13, 2021
Favorite Last year, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart hosts 196 computer vision models, 64 natural language processing (NLP) models, 18 pre-built end-to-end solutions, and 19 example notebooks to
Read More Shared by AWS Machine Learning July 13, 2021
Favorite In this post, we show you how to use Amazon Rekognition and AWS DeepLens to detect, and analyze occupancy in a retail business to optimize workforce utilization. Retailers often need to make decisions to improve the in-store customer experience through personnel management. Having too few or too many employees
Read More Shared by AWS Machine Learning July 9, 2021
Favorite Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation,
Read More Shared by AWS Machine Learning July 9, 2021
Favorite In this post, you learn how to create a MLOps project to automate the deployment of an Amazon SageMaker endpoint with multiple production variants for A/B testing. You also deploy a general purpose API and testing infrastructure that includes a multi-armed bandit experiment framework. This testing infrastructure will automatically
Read More Shared by AWS Machine Learning July 9, 2021
Favorite Hugging Face is the technology startup, with an active open-source community, that drove the worldwide adoption of transformer-based models thanks to its eponymous Transformers library. Earlier this year, Hugging Face and AWS collaborated to enable you to train and deploy over 10,000 pre-trained models on Amazon SageMaker. For more
Read More Shared by AWS Machine Learning July 8, 2021