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.
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
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