With all the turmoil and uncertainty surrounding large Hadoop distributors in the past few weeks, many wonder what’s happening to the data framework we’ve all been working on for years?
With all the turmoil and uncertainty surrounding large Hadoop distributors in the past few weeks, many wonder what’s happening to the data framework we’ve all been working on for years?
Still waiting for ML training to be over? Tired of running experiments manually? Not sure how to reproduce results? Wasting too much of your time on devops and data wrangling?
Yaron Haviv explains serverless and its limitations, providing a hands-on example of using a serverless architecture to simplify data science development and accelerate time to production for data collection, exploration, model training and serving.
Imagine a system where one can easily develop a machine learning model, click on some magic button and run the code in production without any heavy lifting from data engineers…
Data gravity and privacy concerns require federated solutions across public clouds and multiple edge locations. For example, retail stores embed cameras and sensors to track customer purchases, monitor inventory and provide real-time recommendations, but face challenges as forwarding massive volumes of video and sensor data to the cloud for processing is not practical and adds...
The popularity of Kubernetes is exploding. IBM is acquiring RedHat for its commercial Kubernetes version (OpenShift) and VMware just announced that it is purchasing Heptio, a company founded by Kubernetes originators. This is a clear indication that companies are increasingly betting on Kubernetes as their multi-cloud clustering and orchestration technology. At the same time, in...