Extend Kubeflow’s functionality by enabling small teams to build complex real-time data processing and model serving pipelines.
Extend Kubeflow’s functionality by enabling small teams to build complex real-time data processing and model serving pipelines.
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...
While browsing the CNCF Serverless Slack channel recently, I noticed a message; someone needed help writing a function which processes S3 update events. He didn’t want to use AWS Lambda and alternatively was looking for an open source serverless solution over Kubernetes. I took on the challenge of writing, as a response, a function for...