Analytics

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5 machine learning tools to ease software development

AI-driven development tools that provide code auto-completion, code vulnerability detection, and even cutting-edge code generation

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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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10 new tricks your old database can do

You might be surprised by the powerful “NoSQL” features lurking in your old Oracle, SQL Server, MySQL, or PostgreSQL database

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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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Machine learning algorithms explained

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

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Review: Kinetica analyzes billions of rows in real time

GPU database is not only hugely scalable, but integrates graph analysis, location intelligence, and machine learning with standard SQL

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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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TigerGraph review: A graph database designed for deep analytics

Highly parallelized and horizontally scalable, TigerGraph shines for use cases that require multi-hop analytic queries

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SQL tutorial: Learn SQL on MariaDB

Follow this step-by-step guide to install MariaDB, load data, connect to a database, and execute SQL queries including table joins

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AWS piles on the machine learning services

Hosted TensorFlow, pay-as-you-go inference serving, reinforcement learning, and automatic data labeling come to the Amazon cloud

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Machine learning lessons: 5 companies share their mistakes

Machine learning can also go terribly wrong, making you regret that enthusiastic rush to adopt

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Tutorial: Spark application architecture and clusters

Learn how Spark components work together and how Spark applications run on standalone and YARN clusters

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How to use Knime for data science

Free, open-source Knime allows you to visually assemble data processing “nodes” into machine learning, deep learning, and other analytics workflows

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7 cloud services to ease machine learning

Build, train, tune, and deploy machine learning and deep learning models in these end-to-end machine learning clouds

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Built for realtime: Big data messaging with Apache Kafka, Part 1

Apache Kafka scales horizontally and offers much higher throughput than some traditional messaging systems. Get started with installation, then build your first Kafka messaging system

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Real-time data processing with data streaming: new tools for a new era

Real-time data streaming is still early in its adoption, but over the next few years organizations with successful rollouts will gain a competitive advantage

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Machine learning: How to create a recommendation engine

In this excerpt from the book “Pragmatic AI,” learn how to code recommendation engines based on machine learning in AWS, Azure, and Google Cloud

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Why there are no shortcuts to machine learning

As long as companies understand that good data science takes time in an enterprise, and give these people room to learn and grow, they won’t need shortcuts

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