Analytics

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Review: Amazon SageMaker scales deep learning

AWS machine learning service offers easy scalability for training and inference, includes a good set of algorithms, and supports any others you supply

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The era of the cloud database has finally begun

Enterprises are waking up to discover that their database needs have changed dramatically—and that the old-school RDBMS is no longer the best tool

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The new data roles, brought to you by AI

Artificial intelligence is providing market competitiveness through higher productivity and lower costs across many industries. AI is also creating new data roles companies are desperate to fill; read more about these new roles.

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Solidifying security analytics with artificial intelligence knowledge graphs

AI knowledge graphs can augment security analytics by linking knowledge together to pinpoint relationships and patterns related to security issues pertinent to an organization

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Answering the need for speed and scalability: the state of in-memory computing

Mature, cost-effective solutions are powering today’s digital transformation and omnichannel customer experience initiatives

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Making sense of application data management

Business solutions start with clear definitions of data

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TigerGraph: The parallel graph database explained

How TigerGraph achieves fast data ingest, fast graph traversal, and deep link analytics even for large data sets

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These new BI tools bridge the gap between analytics and modern applications

Emerging BI tools let developers be developers, without having to slow down to bother with the data silos they leave in their wake

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Monetizing your models with machine learning solutions

Connecting the business user to the science is a necessary step for companies looking to extend their leadership position

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6 hidden bottlenecks in cloud data migration

From navigating cloud storage options to verifying the data post-transfer, follow these steps to avoid the hazards of cloud migration

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3 requirements of modern archive for massive unstructured data

As the nature and scale of enterprise data changes, secondary storage—especially archive—becomes a huge opportunity to add value to organizational data

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‘To supervise or not to supervise’ is a question at the heart of machine learning

A guide to unsupervised and supervised machine learning and understanding their application by businesses today

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Moving your data analytics to the cloud isn’t so easy

If you dream of modernizing data warehouses—and data marts—in the new platform of the cloud, watch out: It’s not as easy as you might think

App dev trends going hot -- and cold

21 hot programming trends—and 21 going cold

Hot or not? From the web to the motherboard to the training ground, get the scoop on what’s in and what’s out in app dev

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Demystifying machine learning

Businesses need more than a machine learning engine or framework to successfully extract the valuable and actionable insights from their data—and not all machine learning solutions are created equal

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Why data democratization is crucial to your business

Data democratization will catapult companies to new heights of performance—if done right

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Google Cloud tutorial: Get started with Google Cloud

From virtual machines and Kubernetes clusters to serverless functions and machine learning APIs — how to navigate Google’s cloud riches

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Your analytics team needs a liberal arts major

Whether you are a company director, an educator or a parent, you need to think about how to create a diverse workforce in the short and long term

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Data governance 2.0

Part 1 of a conversation with Jason Fishbain, chief data officer, University of Wisconsin at Madison

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3 tips for a successful data strategy

Most modern businesses will survive only if they use data to acquire and retain customers, and if they use data to automate their supply chain or protect their valuable assets

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