Today, May 21st, we received an interesting email from a journalist writing for Fast Company. Apparently, a privacy-focused company audited the app Care19, North Dakota’s COVID-19 contact tracking app, and they found that an anonymous tracking ID generated by the app was sent via API to Bugfender along with other identifiers. While the journalist contacted the app developers, he also gave us the opportunity to comment.

We’re excited to announce the second episode of our podcast, Mobile DevOps is a thing! This episode focuses on different aspects of remote work, Mobile DevOps, and how to remain productive from home, featuring special guest: Joe Birch.

Many enterprise data science teams are using Cloudera’s machine learning platform for model exploration and training, including the creation of deep learning models using Tensorflow, PyTorch, and more. However, training a deep learning model is often a time-consuming process, thus GPU and distributed model training approaches are employed to accelerate the training speed.

Here at Lenses.io, we’re focused on making data technologies such as Apache Kafka and Kubernetes as accessible to every organization as possible. It’s part of our DataOps vision and company DNA. Lenses is built by developers, for developers. We understand the headaches they live with and the challenges they face seemingly have to learn a new data technology every few months. We believe that’s just not the right model.

In today’s enterprise computing landscape, multi-cloud organizations are quickly becoming the norm rather than the exception. By leveraging an API-first strategy with a microservice-based architecture, companies can achieve significant speed to market across multiple clouds. In order to achieve this, container orchestration and a well-designed CI/CD strategy are essential components in this journey.

May 14, 2020 — Allegro AI today announced that it joined the NVIDIA DGX-Ready Software program. Organizations that want to leverage AI to improve products and services often struggle to implement an advanced infrastructure that supports the unique and challenging demands of machine learning and deep learning.