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Turbocharge Your Azure Databricks ML Workflows in Production With Wallaroo

Wallaroo Databricks Integration Demo Video | Wallaroo.AI Blog

In today’s rapidly evolving technological landscape, optimizing ML workflows in production stands as a pivotal challenge for many organizations. However, when harnessed correctly, these workflows can become an organization’s most powerful asset. Dive into this blog as we uncover how to turbocharge your ML workflows in production, ensuring they’re not just efficient, but also robust and ready for real-world challenges.

Solving The Challenge of ML Production Workflows

What if data scientists and ML engineers could go straight from trained models in Databricks to deploying, observing, and optimizing their models in production with a few lines of Python code right from their Databricks notebooks? What if they could easily run A/B tests, work with other Data scientists or ML engineers in collaborative workspaces, have a full audit trail, and monitor drift on all their production ML from an easy-to-use dashboard? And what if they could get their answers 10X faster while using 80% less compute? That’s the value of Wallaroo + Databricks.

Data teams working on Azure Databricks appreciate its powerful data engineering and model development capabilities, but often find that the subsequent steps to get models into production and drive business outcomes require substantial additional engineering and processes. But with our newly launched native integration into the Databricks notebook environment, data scientists and ML engineers can now deploy, observe, troubleshoot, and scale production ML in Azure with just a few simple Python commands via the Wallaroo SDK.

Instead of integrating a variety of ML tools specific to deployment, testing, collaboration, and observability, Wallaroo offers a unified production ML platform that brings those combined capabilities into Databricks without forcing data scientists and engineers to give up what they like about Azure Databricks.

“Wallaroo seamlessly enhances the strengths of developing ML models on Azure Databricks with production ML capabilities right from the Databricks notebooks,” said Younes Amar, Head of Product at Wallaroo.AI. “Integrating production right into their existing tools means more productivity and realizing the value of AI faster for the business.”

Reach out if you would like to see how Wallaroo makes it faster and easier to scale machine learning in your Databricks environment.

You can also join us for our webinar “Driving Rapid Iteration in Production With the Wallaroo + Databricks Integration” where the Wallaroo.AI product team will walk through our new Databricks integration and demo how to easily take a model trained in Databricks and deploy, manage, and monitor via the Wallaroo SDK directly from the Databricks notebook.

You can also test drive it yourself with the following resources:

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