The PII Recognizer is an open source function that can detect PII data in datasets and anonymize the PII entity in the text. Here’s how it works: https://www.iguazio.com/blog/how-to-mask-pii-before-llm-training/

In this demo we will be showcasing how we used LLMs to turn call center conversation audio files of customers and agents into valueable data in a single workflow orchastrated by MLRun. MLRun will be automating the entire workflow, auto-scale resources as needed and automatically log and parse values between the workflow different steps. By...

The influx of new tools like ChatGPT spark the imagination and highlight the importance of Generative AI and foundation models as the basis for modern AI applications. However, the rise of generative AI also brings a new set of MLOps challenges. Challenges like handling massive amounts of data, large scale computation and memory, complex pipelines,...

Here’s how to use Databricks for model training and tracking while using MLRun to deploy model serving. Read the full tutorial here.

The open source ML tooling ecosystem has become vast in the last few years, with many tools covering different aspects of the complex and expansive process of building, deploying and managing AI in production. Some tools overlap in their capabilities while others complement each other nicely. In part because AI/ML is still an emerging and...

Using Hugging Face and MLRun together significantly shortens the model development, training, testing, deployment and monitoring process. By getting your models to production faster, you can answer business needs faster while saving resources. Read the blog here, and watch the full demo here.

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.