MLRun basics

What is MLRun?
MLOps development flow
Quick-Start Guide
Tutorials
- Getting-Started Tutorial
- Converting Research Notebook to Operational Pipeline With MLRun
Installation and setup guide

Concepts

Projects
MLRun serverless functions
Data stores and feature store
Runs, functions, and workflows
Artifacts and models
Deployment and monitoring

Working with data

Feature Store: Data ingestion
Feature Store: Data retrieval
Feature Store tutorials
- Feature store example (stocks)
- Feature store end-to-end demo

Develop functions and models

Creating and using functions
Run, track, and compare jobs

Deploy ML applications

Real-time serving pipelines (graphs)
Model serving pipelines
Model monitoring overview (beta)
- Model monitoring overview (beta)
- Enable model monitoring (beta)
CI/CD, rolling upgrades, git
- Github/Gitlab and CI/CD integration

References

API Index
API By Module
Command-Line Interface (Tech preview)
Examples

By Iguazio

Feature Store: Data ingestion¶

Learn how to ingest data into the feature store, with transformations, using the supported engines.

In this section

Feature sets
Feature set transformations
Using the Spark execution engine

Deployment and monitoring Feature sets

By Iguazio
© Copyright 2022, Iguazio.