A Technology Broker and Management Company Specializing in KM Systems
Favorite Amazon Rekognition Custom Labels is a fully managed computer vision service that allows developers to build custom models to classify and identify objects in images that are specific and unique to your business. Rekognition Custom Labels doesn’t require you to have any prior computer vision expertise. You can get
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Shared by AWS Machine Learning April 7, 2022
Favorite Research over the past few years has shown that machine learning (ML) models are vulnerable to adversarial inputs, where an adversary can craft inputs to strategically alter the model’s output (in image classification, speech recognition, or fraud detection). For example, imagine you have deployed a model that identifies your
Read More Shared by AWS Machine Learning April 6, 2022
Favorite The built-in Amazon SageMaker XGBoost algorithm provides a managed container to run the popular XGBoost machine learning (ML) framework, with added convenience of supporting advanced training or inference features like distributed training, dataset sharding for large-scale datasets, A/B model testing, or multi-model inference endpoints. You can also extend this
Read More Shared by AWS Machine Learning April 6, 2022
Favorite Customer service calls require customer agents to have the customer’s account information to process the caller’s request. For example, to provide a status on an insurance claim, the support agent needs policy holder information such as the policy ID and claim number. Such information is often collected in the
Read More Shared by AWS Machine Learning April 5, 2022
Favorite Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon Kendra
Read More Shared by AWS Machine Learning April 5, 2022
Favorite As more organizations move to machine learning (ML) to drive deeper insights, two key stumbling blocks they run into are labeling and lifecycle management. Labeling is the identification of data and adding labels to provide context so an ML model can learn from it. Labels might indicate a phrase
Read More Shared by AWS Machine Learning April 5, 2022
Favorite There is a clear view than knowledge lies in the “walls” and the “hallways” between the “rooms” of an organisation. Here are some of the implications of this view for Knowledge Management. Image from wikimedia commons This blog post was inspired by a post from Nancy Dixon entitled Where Is
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Shared by Nick Milton April 4, 2022
Favorite For customers looking to implement a GxP-compliant environment on AWS for artificial intelligence (AI) and machine learning (ML) systems, we have released a new whitepaper: Machine Learning Best Practices in Healthcare and Life Sciences. This whitepaper provides an overview of security and good ML compliance practices and guidance on
Read More Shared by AWS Machine Learning April 2, 2022
Favorite Data science and data engineering teams spend a significant portion of their time in the data preparation phase of a machine learning (ML) lifecycle performing data selection, cleaning, and transformation steps. It’s a necessary and important step of any ML workflow in order to generate meaningful insights and predictions,
Read More Shared by AWS Machine Learning April 1, 2022
Favorite Amazon SageMaker Autopilot helps you complete an end-to-end machine learning (ML) workflow by automating the steps of feature engineering, training, tuning, and deploying an ML model for inference. You provide SageMaker Autopilot with a tabular data set and a target attribute to predict. Then, SageMaker Autopilot automatically explores your
Read More Shared by AWS Machine Learning March 30, 2022