ML-Powered Databricks Cluster
Optimizations for All

Granular compute metrics are obtained by retrieving cluster logs beyond what Databricks exposes in their system tables

Integrate with Databricks Workflows or Airflow to plug Gradient into how your company runs your infrastructure

Easy metrics gathering as Gradient does the heavy lifting for you and automatically compiles and links information across both Databricks and cloud environments

Gradient connects to your Databricks workspace behind the scenes to make importing and setting up job clusters as easy as a single click

Non-invasive webhook integration is used to link your environment with Gradient without any modifications to your code or workflows

Historical log information is used to train custom models for each of your jobs.  Since no two jobs are alike, custom models are critical to optimizing at scale.

Accuracy is ensured by training on real job performance data

Stability is obtained with small incremental changes and monitoring to ensure reliable performance

View recommendations in the Gradient UI for your approval before any changes are actually made

Click to approve and apply a single recommendation so you are always in control

Focus on business goals by allowing Gradient to constantly improve your job clusters to meet your ever changing business needs

Optimize at scale with auto apply, no need to manually analyze individual jobs – just watch Gradient get to work across all of your jobs

Free your engineers from manually tweaking cluster configurations and allowing them to focus on more important work