Ever wonder if it’s possible to train machine learning (ML) models with regulated data which can’t be sent to the cloud? Has your edge solution gathered so much data that it just doesn’t make sense to send it all to
the cloud?
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?
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?
We need to break free of the belief that the dirt road we are on is the only road possible and, instead, think about how to pave it.
We use a Nuclio serverless function to “listen” to a Kafka stream and then ingest its events into our time series table.
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…
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
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
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,
The serverless revolution isn’t coming soon, it’s already here! Our recent Serverless NYC 2018 show was packed due to a wide range of great speakers.