Amazon SageMaker supports all the popular deep learning frameworks, including TensorFlow. Over 85% of TensorFlow projects in the cloud run on AWS. Many of these projects already run in Amazon SageMaker. This is due to the many conveniences Amazon SageMaker provides for TensorFlow model hosting and training, including fully managed
Rated the number 2 Knowledge Management authority by Agilence, the social authority ranker. Second only to the legendary Stan Garfield (congratulations Stan) View Original Source (nickmilton.com) Here.
The Experts can sometimes be resistant to KM, seeing it as a threat or a burden, with little personal reward. How can we address this? Image from wikimedia commons Many clients we speak to are having real problems recruiting the expert knowledge holders to the concept of Knowledge Management. Even in
People often make a big thing about learning from failure, but learning from success is just as important, and can often be overlooked. Crossroads by Chris Potter on Flickr and stockmonkeys.com The impetus to revisit this topic came from a one-liner post on LinkedIn by Oleg Vishnepolsky reading “Sometimes I win,
Is KM a Science? Is KM a Philosophy? No – it’s a Management Discipline. And here is why that is a useful viewpoint. I have met many people over the years who treat Knowledge Management as something entirely unique – a philosophy, almost, or a “world-view”. For these people, there
Confirmation Bias is one of the most pernicious cognitive biases, and is a major challenge to Knowledge Management. See it in action below. Confirmation bias is a powerful cognitive bias, which means that people Tend to select evidence that supports what they already believe, and Set up tests that confirm
This post offers a dive deep into how to use Amazon Elastic Inference with Amazon Elastic Kubernetes Service. When you combine Elastic Inference with EKS, you can run low-cost, scalable inference workloads with your preferred container orchestration system. Elastic Inference is an increasingly popular way to run low-cost inference workloads
After you’ve trained and exported a TensorFlow model, you can use Amazon SageMaker to perform inferences using your model. You can either: Deploy your model to an endpoint to obtain real-time inferences from your model. Use batch transform to obtain inferences on an entire dataset stored in Amazon S3. In
Launched at AWS re:Invent 2018, Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for your machine learning models. Amazon SageMaker Ground Truth offers easy access to public and private human labelers, and provides them with built-in workflows and interfaces for common labeling tasks. Additionally, Amazon SageMaker
The default approach to managing knowledge which many companies use, is to keep knowledge in people’s heads, and to move the knowledge where it is needed by moving the people, not by transferring the knowledge. Homer Simpson’s head by SOCIALisBETTER on Flickr In this old model, knowledge is owned and
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Shared by AWS Machine Learning September 13, 2019
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