Big Data

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How to do real-time analytics across historical and live data

5 in-memory computing platform capabilities that support analytical processing of both data lake data and operational streams

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HPE plus MapR: Too much Hadoop, not enough cloud

MapR gives HPE superior big data analytics technology and expertise, but not what HPE needs most

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The best machine learning and deep learning libraries

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models

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The best machine learning and deep learning libraries

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models

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TensorFlow 2 review: Easier machine learning

Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment

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The data lake is becoming the new data warehouse

Platforms like AWS Lake Formation and Delta Lake point toward a central hub for decision support and AI-driven decision automation

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Time series analysis with KNIME and Spark

Train and evaluate a simple time series model using a random forest of regression trees and the NYC Yellow taxi data set

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Supervised learning explained

Supervised learning turns labeled training data into a tuned predictive model

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What is TensorFlow? The machine learning library explained

TensorFlow is a Python-friendly open source library for numerical computation that makes machine learning faster and easier

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Hadoop runs out of gas

As big data customers flee complexity and embrace the cloud, Hadoop vendors are sputtering

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Hadoop runs out of gas

As big data customers flee complexity and embrace the cloud, the Hadoop vendors are sputtering

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What is natural language processing? AI for speech and text

Deep learning has improved machine translation and other natural language processing tasks by leaps and bounds

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What is deep learning? Algorithms that mimic the human brain

Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data

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4 reasons big data projects fail—and 4 ways to succeed

Nearly all big data projects end up in failure, despite all the mature technology available. Here's how to make big data efforts actually succeed

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What is machine learning? Intelligence derived from data

Machine learning algorithms learn from data to solve problems that are too complex to solve with conventional programming

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Machine learning algorithms explained

Machine learning uses algorithms to turn a data set into a model. Which algorithm works best depends on the problem

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Delta Lake gives Apache Spark data sets new powers

A new open source project from Databricks adds ACID transactions, versioning, and schema enforcement to Spark data sources that don't have them

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Pub/sub messaging: Apache Kafka vs. Apache Pulsar

Apache Kafka set the bar for large-scale distributed messaging, but Apache Pulsar has some neat tricks of its own

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Apache Kafka vs. Apache Pulsar: How to choose

Apache Kafka set the bar for large-scale distributed messaging, but Apache Pulsar has some neat tricks of its own

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IBM preps Watson AI services to run on Kubernetes

IBM Watson services arrive in versions that can run on the public cloud or on privately hosted container infrastructure

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