Remove 2010 Remove Clustering Remove ML
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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning Blog

Amazon SageMaker Canvas Amazon SageMaker Canvas is a visual machine learning (ML) service that enables business analysts and data scientists to build and deploy custom ML models without requiring any ML experience or having to write a single line of code. Through Atlas Data Federation, data is extracted into Amazon S3 bucket.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

AWS 93
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Structural Evolutions in Data

O'Reilly Media

A basic, production-ready cluster priced out to the low-six-figures. A company then needed to train up their ops team to manage the cluster, and their analysts to express their ideas in MapReduce. Plus there was all of the infrastructure to push data into the cluster in the first place. Goodbye, Hadoop. And it was good.

Hadoop 101
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Intuitive robotic manipulator control with a Myo armband

Mlearning.ai

It turned out that a better solution was to annotate data by using a clustering algorithm, in particular, I chose the popular K-means. So I simply run the K-means on the whole dataset, partitioning it into 4 different clusters. The label of a cluster was set as a label for every one of its samples. 2010, doi: 10.1109/TBME.2010.2060723.

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Analyzing the history of Tableau innovation

Tableau

Clustered under visual encoding , we have topics of self-service analysis , authoring , and computer assistance. Nov 2010), which allowed users to drag and drop multiple tables on one sheet. Gestalt properties including clusters are salient on scatters. Visual encoding is key to explaining ML models to humans.

Tableau 145
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Analyzing the history of Tableau innovation

Tableau

Clustered under visual encoding , we have topics of self-service analysis , authoring , and computer assistance. Nov 2010), which allowed users to drag and drop multiple tables on one sheet. Gestalt properties including clusters are salient on scatters. Visual encoding is key to explaining ML models to humans.

Tableau 98