IBM Watson Data Platform

June 25, 2018

IBM Cloudant at the Tony Awards

IBM has been a proud partner of the American Theatre Wing's Tony Awards® since 2000. IBM proudly designs, builds and hosts the official website of the Tony Awards®, at in partnership with Tony Award Productions. The Tony Awards® are named for Antoinette Perry, a former leader of the American Theatre Wing. The Tony Award statuette and medallion is a highly coveted award in the professional entertainment scene. The Tony Awards® debuted on April 6, 1947 at a dinner in the Grand Ballroom of New York City's Waldorf Astoria hotel to celebrate theatre excellence. The annual Tony Awards® ceremony and entertainment program is a greatly anticipated highlight of the New York gala theatre season. The awards evening is normally hosted at New York City's Radio City Music Hall and is broadcast live nationally and internationally by CBS every June.

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April 3, 2018

IBM Analytics Engine is now available in the London DC

The IBM Analytics Engine team is excited to announce the General Availability (GA) of IBM Analytics Engine, the next generation of IBM’s Apache Spark and Apache Hadoop cloud service in the London DC.

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March 19, 2018

A predictive Machine Learning model from Build to Retrain

This post is an excerpt from our solution tutorial that walks you through the process of building a predictive machine learning model, deploying it as an API to be used in applications, testing the model and retraining the model with feedback data. All of this happening in an integrated and unified self-service experience on IBM Cloud.

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February 8, 2018

Taming your neural networks: how controlled experimentation can help you build better machine learning models

Businesses today are eager to harness machine learning and deep learning for competitive advantage—yet few businesspeople realize that building a machine learning model or neural network is a marathon, not a sprint.

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February 7, 2018

Getting closer to the source: the power of self-service data preparation

IBM Data Refinery, a feature of Watson Data Platform, helps reduce reliance on IT and give knowledge workers faster access to high-quality data

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January 30, 2018

Spot the Difference: The role of visualization in effective data preparation

It’s clear to many in the business community that big data analytics is the next mountain to climb. However, while the final destination may be obvious to everyone, ascending to the summit will need careful planning. In many cases, even reaching base camp can be a tortuous process.

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November 20, 2017

Closing the loop: the case for building a coherent platform for data science and machine learning

Many organizations have started to explore the value that machine learning can bring—from illuminating previously “dark data” such as images and videos, to creating models that help to guide or even automate business decision-making. However, very few companies have gone beyond pilots and prototypes, or made the transition from one-off projects to a scalable, repeatable workflow. Too often, machine learning exists in a bubble of its own, instead of being understood in the context of the broader data science workflow.

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November 2, 2017

Accelerate to AI, data-driven business with IBM Watson Data Platform

IBM announced a series of upgrades and new offerings to Watson Data Platform, an integrated set of tools, services and data in the IBM Cloud that enables data scientists, developers and business teams to gain intelligence from data.

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November 2, 2017

Powerful data transformation and visualization with IBM Data Refinery

As a data scientist, you are probably spending a lot of time cleansing, shaping and formatting your data before you can do the analysis. According to a recent report, data scientists spend up to 80 percent of their time finding and preparing data. And 57 percent of data scientists said that cleaning and organizing data is the least enjoyable part of their job. The problem isn’t just limited to data scientists. Business analysts face similar struggles to obtain the data they need to build reports—often having to wait weeks for their IT team to extract data from the source systems.

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