Modern manufacturing technology is starting to incorporate machine learning throughout the production process. Predictive algorithms are being used to plan machine maintenance adaptively rather than on a fixed schedule. Meanwhile, quality control is becoming more and more automated, with adaptive algorithms that learn to recognize correctly manufactured products and reject defects. In this post we […]
A few days ago we released Seahorse 1.4, an enhanced version of our machine learning, Big Data manipulation and data visualization product. This release also comes with an SDK – a Scala toolkit for creating new custom operations to be used in Seahorse. As a showcase, we will create a custom Geospatial operation with GeoJson […]
In the latest Seahorse release we introduced the scheduling of Spark jobs. We will show you how to use it to regularly collect data and send reports generated from that data via email. Use case Let’s say that we have a local meteo station and the data from this station is uploaded automatically to Google […]
Internal validation is a useful tool for comparing results of experiments performed by team members in any business or research task. It can also be a valuable complement of public leaderboards attached to machine learning competitions on platforms like Kaggle. In this post, we present how to build an internal validation leaderboard using Python scripts […]
We’re happy to announce that a new version of Neptune became available this month. The latest 1.3 release of deepsense.ai’s machine learning platform introduces powerful new features and improvements. This release’s key added features are: integration with TensorFlow and running Neptune experiments in Docker containers.
deepsense.io is pleased to announce the results of its multi-month machine learning-based collaboration with the United Nations Office of Information and Communications Technology (ICT).
Underground mining poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. An automatic system for predicting and alerting against such dangerous events is of utmost importance – and also a great challenge for data scientists and their machine learning models. This was the inspiration for the organizers of AAIA’16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines.
Menlo Park, CA and Warsaw-based CodiLime ranks 2nd in this year’s Deloitte Central Europe Technology Fast 50 list of the region’s fastest-growing tech companies. CodiLime provides IT consulting and software engineering services in three core areas: networks, security as well as Big Data & deep learning through its deepsense.io subsidiary.
deepsense.io announced the launch of Neptune – its innovative machine learning platform for managing multiple data science experiments. The premiere took place at the inaugural A.I. Conference (September 26-27) as well as this year’s Strata + Hadoop World (September 27-29), both being held in New York City.
Latest version of deepsense.io’s flagship Big Data product now features Spark 2.0 support, external Spark clusters, and custom operations and notebooks in R