Training XGBoost with R and Neptune

We present the training of an XGBoost model and evaluation of the results in an example analysis of bank customer data, using R with Neptune.

deepsense.io helps improve the UK’s satellite defence and security intelligence

deepsense.io helps improve the UK’s satellite defence and security intelligence

The deepsense.io team makes a significant contribution to the satellite imagery project hosted at Kaggle.com aimed at increasing the efficiency of the decision-making process for the UK’s defence and security.

Why allow Cloudera’s vendor locking instead of using deepsense.io’s Neptune?

Why allow Cloudera’s vendor locking instead of using deepsense.io’s Neptune?

deepsense.io congratulates Cloudera on acquiring Sense.io and their recent launch of the Cloudera Data Science Workbench. “Well done, but too late!” – says deepsense.io’s CEO Tomasz Kulakowski, and asks: “Why allow Cloudera’s vendor locking instead of using deepsense.io’s Neptune – a DevOps platform for machine and deep learning experiments, which works with any Hadoop distribution (incl. Hortonworks, Cloudera and MapR) and any cloud provider (such as AWS, MS Azure, Google Cloud Platform) as well as available on-premises?”

Neptune machine learning platform: grid search, R & Java support

In February we released a new version of Neptune, our machine learning platform for data scientists, supporting them in more efficient experiment management and monitoring. The latest 1.4 release introduces new features, like grid search — a hyperparameter optimization method and support for R and Java programming languages. Grid Search The first major feature introduced […]