ML teams should be able to achieve MLOps by using their preferred frameworks, platforms, and languages to experiment, build & train their models.
Here’s the problem: we are always under pressure to reduce the time it takes to develop a new model, while datasets only grow in size. Running a training job on a single node is pretty easy, but nobody wants to wait hours and then run it again, only to realize that it wasn’t right to begin with.
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?
2020 will be about simplifying the way from data science to production, with an emphasis on bringing real – and scalable - business value.
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.