TensorFlow and deep learning are things that corporations must now embrace. The coming flood of audio, video, and image data and their applications are key to success.
Apache Zeppelin — an open-source data analytics and visualization platform — helps us analyze the data to gain insight and to improve and enhance business decisions.
Learn how to perform anomaly detection using Kafka Streams with an example of a loan payment website that needs to send an alert if the payment is too high.
See how to get started with writing stream processing algorithms using Apache Flink. by reading a stream of Wikipedia edits and getting some meaningful data out of it.
Some quick stats: 656 million tweets go out per day, and 15,220,700 texts are sent every minute. This makes for LOTS of data. Read on for more shocking stats!
If you've been following software development news recently you probably heard about the new project called Apache Flink. I've already written about it a bit...
The variable selection process in the credit score modeling process is critical to finding key information. Learn how to do it to get a good understanding of your data!
If you have often wondered to yourself about the difference between machine learning and deep learning, read on to get a detailed comparison in simple layman language.
More than a third of the Fortune 500 companies now use Kafka in production — and for good reason. In this article, learn how to track real-time activity using Kafka.
Data scientists are responsible for designing and developing accurate, useful, and stable models. This is especially important when it comes to credit risk models.
GPUs can accelerate the training of machine learning models. In this post, explore the setup of a GPU-enabled AWS instance to train a neural network in TensorFlow.
Learn how data is analyzed and boiled down to a single value — a credit score — using statistical, machine learning, and predictive analytics techniques.
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