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HawkEye 360 develop maritime security and vessel monitoring capability

21 Apr 2021 (Last Updated April 21st, 2021 12:23)
HawkEye 360 has developed new maritime security and vessel monitoring capabilities that combine its radio frequency (RF) geolocation services with Amazon Web Services’ (AWS) machine learning (ML) model.
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HawkEye 360 have developed maritime security and vessel monitoring capabilities using AWS’ machine learning. Credit: JF Martin on Unsplash.
HawkEye 360 has developed new maritime security and vessel monitoring capabilities that combine its radio frequency (RF) geolocation services with Amazon Web Services’ (AWS) machine learning (ML) model.
The maritime security and vessel monitoring capabilities will be integrated into the HawkEye 360 portfolio of products.
Using underlying vessel characteristics and behaviours, it becomes possible to predict whether a given vessel is likely to engage in similar activities as sanctioned vessels. ....

Tim Pavlick , Amazon Web Service , Sagemaker Autopilot , நேரம் பாவ்லிக் , அமேசான் வலை சேவை ,

Review: AWS AI and Machine Learning stacks up

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Review: AWS AI and Machine Learning stacks up
Amazon Web Services provides an impressively broad and deep set of machine learning and AI services, rivalling Google Cloud and Microsoft Azure.
Credit: Dreamstime
Amazon Web Services claims to have the broadest and most complete set of machine learning capabilities. I honestly don’t know how the company can claim those superlatives with a straight face: Yes, the AWS machine learning offerings are broad and fairly complete and rather impressive, but so are those of Google Cloud and Microsoft Azure. ....

Sagemaker Feature , Amazon Web Services , Amazon Sagemaker Feature , Machine Learning Services , Web Services , Google Cloud , Sagemaker Clarify , Amazon Sagemaker , Maker Clarify , Data Wrangler , Sagemaker Studio , Sagemaker Model Monitor , Amazon Monitron , Ground Truth , Feature Store , Model Monitor , Distributed Training , Apache Spark , Maker Studio , Sagemaker Notebooks , Sagemaker Jumpstart , Microsoft Azure Machine Learning , Amazon Sagemaker Notebook , Amazon Sagemaker Studio , Sagemaker Autopilot , Amazon Sagemaker Clarify ,

Review: AWS AI and Machine Learning stacks up

Review: AWS AI and Machine Learning stacks up
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Sagemaker Feature , Amazon Web Services , Amazon Sagemaker Feature , Machine Learning Services , Web Services , Google Cloud , Sagemaker Clarify , Amazon Sagemaker , Maker Clarify , Data Wrangler , Sagemaker Studio , Sagemaker Model Monitor , Amazon Monitron , Ground Truth , Feature Store , Model Monitor , Distributed Training , Apache Spark , Maker Studio , Sagemaker Notebooks , Sagemaker Jumpstart , Microsoft Azure Machine Learning , Amazon Sagemaker Notebook , Amazon Sagemaker Studio , Sagemaker Autopilot , Amazon Sagemaker Clarify ,

Review: AWS AI and Machine Learning stacks up

Amazon Web Services provides an impressively broad and deep set of machine learning and AI services, rivaling Google Cloud and Microsoft Azure. ....

Sagemaker Feature , Amazon Web Services , Amazon Sagemaker Feature , Machine Learning Services , Google Cloud , Sagemaker Clarify , Amazon Sagemaker , Maker Clarify , Data Wrangler , Sagemaker Studio , Sagemaker Model Monitor , Amazon Monitron , Ground Truth , Feature Store , Model Monitor , Distributed Training , Apache Spark , Maker Studio , Sagemaker Notebooks , Sagemaker Jumpstart , Microsoft Azure Machine Learning , Amazon Sagemaker Notebook , Amazon Sagemaker Studio , Sagemaker Autopilot , Amazon Sagemaker Clarify , Sagemaker Python ,

8 databases supporting in-database machine learning

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In my August 2020 article, “How to choose a cloud machine learning platform,” my first guideline for choosing a platform was, “Be close to your data.” Keeping the code near the data is necessary to keep the latency low, since the speed of light limits transmission speeds. After all, machine learning especially deep learning tends to go through all your data multiple times (each time through is called an
epoch).
I said at the time that the ideal case for very large data sets is to build the model where the data already resides, so that no mass data transmission is needed. Several databases support that to a limited extent. The natural next question is, which databases support internal machine learning, and how do they do it? I’ll discuss those databases in alphabetical order. ....

Server Machine Learning Services , Machine Learning Services For Python , Amazon Sagemaker Autopilot , Sagemaker Autopilot , Apache Arrow , Google Cloud , Google Cloud Machine Learning , Streaming Data Warehouse , Machine Learning Services , Big Data , Big Data Clusters , Azure Machine Learning , Cloud Infrastructure , Data Science , Oracle Cloud , Oracle Accelerated Data Science , Oracle Cloud Infrastructure , Data Flow , Autonomous Data Warehouse , Analytics Platform , சேவையகம் இயந்திரம் கற்றல் சேவைகள் , இயந்திரம் கற்றல் சேவைகள் க்கு பைதான் , அப்பாச்சி அம்பு , கூகிள் மேகம் , கூகிள் மேகம் இயந்திரம் கற்றல் , ஸ்ட்ரீமிங் தகவல்கள் கிடங்கு ,