analytics

Lifelong (machine) learning: how automation can help your models get smarter over time

Imagine you’re interviewing a new job applicant who graduated top of their class and has a stellar résumé. They know everything there is to know about the job, and has the skills that your business needs. There’s just one catch: from the moment they join your team, they’ve vowed never to learn anything new again. You probably wouldn’t make that hire, because you know that lifeMachine Learning Brainlong learning is vital if someone is going to add long-term value to your team. Yet when we turn to the field of machine learning, we see companies making a similar mistake all the time. Data scientists work hard to develop, train and test new machine learning models and neural networks. However, once the models get deployed, they don’t learn anything new. After a few weeks or months, become static and stale, and their usefulness as a predictive tool deteriorates.

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How to manage time-series data with InfluxCloud

InfluxData is proud to announce that its InfluxCloud managed service for time-series data is now available on Bluemix. Rooted in open source, the InfluxData platform is built specifically for metrics, events, and other time-based data. In other words, a true modern time-series platform.

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Clouded vision: How financial institutions can gain a clearer view of cloud computing opportunities

While most industries have enthusiastically embraced cloud computing, there is still a widespread perception in the financial services sector that adopting cloud services is either too risky from a security or availability perspective, or outright impossible under current regulatory conditions.

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Learn How to use R with Databases

Do you want to leverage the power of R to unlock the value of data in relational databases? Are you a database professional and looking to get skilled in data science? Are you a data scientist and hitting the limits of R while trying to analyze very large data sets? If you answered yes to […]

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Query many data sources as one: IBM Queryplex for data analytics

Queryplex runs advanced analytics (SQL, Python, R, PySpark, etc) across many devices and data sources as though they are a single consolidated data repository. The technology can be used to erase data silos of multiple databases (e.g. Oracle, DB2, PostgreSQL, Netezza), or compute analytics across tens of thousands of distributed Internet of Things devices where data may be stored in smaller repositories (text files, Excel spreadsheets, Informix, MySQL). Queryplex let's you query many data sources at once with a single statement, whether they are large repositories, small devices, or any combination of them.

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Migrate Analytics for Apache Spark Notebooks to the IBM Data Science Experience

Hello IBM Analytics for Apache Spark Users, We will be discontinuing support for the Jupyter notebooks on Bluemix as of April 6, 2017. As an IBM Analytics for Apache Spark user, we’d like you to leverage the latest Jupyter Notebooks on the Data Science Experience by migrating your notebooks (Spark 1.4.1 and later versions). Please […]

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Announcing the DevOps Insights beta

The IBM® Cloud DevOps Insights service is now available as an open beta offering.

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Why Interconnect Should Be On Your List of Developer Events to Attend in 2017

If you have never been to IBM’s Interconnect, you might be wondering “With all of the other conferences and events focused on developers and technical topics, what makes Interconnect different?” Well, let me share some of my experiences so you can see for yourself why it is worth attending.

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Connecting to streams: What you need to know

This article describes three options for how to deal with Streaming Analytics Service applications that currently depend on incoming connections.

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