Deep learning applications are now truly amazing, ranging from image detection to natural language processing (for example, automatic translation). It gets even more amazing when Deep Learning becomes unsupervised or is able to generate self-representations of the data.
This post is about how to ingest data from different kinds of file systems by means of Kafka-Connect using a connector I’ve forged recently.
On March the 26th 2012, James Cameron and his submarine craft, Deepsea Challenger, explored the depths of the ocean down to 11km under sea level at 11.329903°N 142.199305°E, an infinitesimal point on the surface of the Earth’s vast Oceans.
When we want to fit a Machine Learning (ML) model to a big dataset, it is often recommended to carefully pre-process the input data in order to obtain better results.
This will be the last installment in the “Continuous Delivery in depth” series. After the good and the bad, here comes the ugly. Ugly because of the amount of changes required: a pull request with 308 commits was merged adding 2932 lines, whilst removing a whooping 10112.
Companies have come to realize of late that the real value of their business is data. There has been a rush to create huge Data Lakes to store the enormous amounts of data available inside each company.