From AutoML to AutoMLOps: This post shows you how to automate engineering tasks like logging and tracking, so that your code is automatically ready for production. Read more here.

This article walks you through the steps to run a Jenkins server in docker and deploy the MLRun project using Jenkins pipeline. 

Here’s how to build simple AI applications that leverage MLRun and some pre-built ML models and allow you to interact with a UI in Streamlit to visualize the results. Check out the full tutorial here and then build your own!

Here’s a simple tutorial on how to turn your existing model training code into an MLRun job and get the benefit of all the experiment tracking, plus more. 

MLRun v0.10 is now generally available. This release includes: Snowflake is now supported as a datasource for the feature store Consumer group streaming report has been added An option to configure pods priority and spot instances from the UI has been added Masking of sensitive data in the UI has been enhanced Configuration of CPU, GPU,...

A simple tutorial showing how to build an image classifier AI app in 5 minutes with MLRun, including a look under the hood to see how everything is connected.

MLRun v0.10 is now generally available. This release includes: New project-level menu. When accessing a project in the Projects page, the new menu on the left provides access to all aspects of the project, including the Project Settings. Improved UI performance when fetching objects from the MLRun DB. This relates mainly to the jobs and projects...

In this session, Yaron Haviv, MLRun Co-Founder, discusses how to enable continuous delivery of machine learning to production using Git-based ML pipelines (Github Actions) with hosted training and model serving environments. He touched upon how to leverage Git to solve rigorous MLOps needs: automating workflows, reviewing models, storing versioned models as artifacts, and running CI/CD...

MLRun v0.9 is now generally available. This release includes: MySQL is now the underlying database in MLRun, replacing SQLite. Feature store updates: Spark is now certified as an engine Data can be ingested incrementally Google BigQuery is now supported as a data source UI: The new Kubeflow Pipeline report is included in the Projects page Projects...

MLRun v0.8 is now generally available. This release includes: Projects now have members (users, user groups), thereby controlling access to view and manage the project. (Available with the Iguazio MLOps Platform 3.2)  The new Settings dialog in the Projects page has two tabs: General, and Secrets, to manage general settings and project secrets. The project...