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?
Here's how to turn your existing model training code into an MLRun job and get the benefit of all the experiment tracking, plus more.
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?
Deep learning use cases are one of the toughest to tackle, and the complexities of this subset of ML need some mitigation. Here's how MLRun can do just that, automating and orchestrating the entire DL pipeline.
Feature stores enable data scientists to reuse features instead of rebuilding these features again and again for different models, saving them valuable time and effort.
In part 4 of the guide to using Azure ML with the Iguazio feature store, we cover model serving on-prem and hybrid cloud, and model monitoring.
In part 3 of the guide to using Azure ML with the Iguazio feature store, we cover model training via Azure ML, while leveraging Iguazio.
In part 2 of the guide to using Azure ML with the Iguazio feature store, we cover data ingestion and transformation into the feature store.