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Databricks Model Serving V2

Posted Sep 30
# Databricks
# Deployment
# Real-time ML Models
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SPEAKER
Rafael Pierre
Rafael Pierre
Rafael Pierre
Solutions Architect @ Databricks

Rafael has worked for 15 years in data-intensive fields within finance in multiple roles: software engineering, product management, data engineering, data science, and machine learning engineering.

At Databricks, Rafael has fun bringing all these topics together as a Solutions Architect to help our customers become more and more data-driven.

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Rafael has worked for 15 years in data-intensive fields within finance in multiple roles: software engineering, product management, data engineering, data science, and machine learning engineering.

At Databricks, Rafael has fun bringing all these topics together as a Solutions Architect to help our customers become more and more data-driven.

+ Read More
SUMMARY

From our experience helping customers in the Data and AI field, we learned that the most challenging part of Machine Learning is deploying it. Putting models into production is complex and requires additional pieces of infrastructure as well as specialized people to take care of it - this is especially true if we are talking about real-time REST APIs for serving ML models.

With Databricks Model Serving V2, we introduce the idea of Serverless REST endpoints to the platform. This allows teams to easily deploy their ML models in a production-grade platform with a few mouse clicks (or lines of code 😀).

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# Responsible AI
# Explainable AI
# AI Model Governance