What is the primary benefit of this Databricks Model Serving skill?

It provides ready-to-use code patterns for deploying ML models with advanced features like traffic splitting and auto-scaling, ensuring production reliability.

Does it support cost optimization?

Yes, it implements best practices for 'scale-to-zero' and selecting appropriate workload sizes to minimize Databricks compute costs.

Can I use this skill for A/B testing?

Yes, the skill includes specific patterns for deploying multiple model versions and configuring traffic percentages for controlled experiments.

What tools are required to use these patterns?

These patterns primarily utilize the Databricks SDK for Python and standard Python HTTP libraries for endpoint testing.

Databricks Model Serving

Name: Databricks Model Serving
Author: vivekgana

byvivekgana

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Data Science & ML

Deploys and manages production-grade machine learning models on Databricks with support for A/B testing and auto-scaling.

About

This skill provides standardized implementation patterns for Databricks Model Serving, enabling developers to deploy machine learning models with production-ready configurations. It covers essential MLOps workflows including automated endpoint creation via the Databricks SDK, traffic-splitting for A/B testing, and cost-optimization through scale-to-zero features. By using this skill, teams can ensure consistent deployment strategies, optimize inference latency, and implement robust rollback plans for their AI applications.

Key Features

Auto-scaling and scale-to-zero for cost efficiency
Configurable A/B testing and traffic-splitting patterns
Best practices for workload sizing and performance monitoring
2 GitHub stars
Programmatic endpoint deployment using Databricks SDK
Standardized endpoint testing and invocation templates

Use Cases

Running live canary deployments to compare model versions
Transitioning ML models from development to production endpoints
Optimizing cloud costs for low-traffic or intermittent inference tasks

About

Key Features

Auto-scaling and scale-to-zero for cost efficiency
Configurable A/B testing and traffic-splitting patterns
Best practices for workload sizing and performance monitoring
2 GitHub stars
Programmatic endpoint deployment using Databricks SDK
Standardized endpoint testing and invocation templates

Use Cases

Running live canary deployments to compare model versions
Transitioning ML models from development to production endpoints
Optimizing cloud costs for low-traffic or intermittent inference tasks