From 441d41a44a3a230c51041429ccb969ac95d90ad5 Mon Sep 17 00:00:00 2001 From: ben Date: Thu, 15 Feb 2024 17:18:12 -0800 Subject: [PATCH] refined scribs platform section --- _posts/2024-02-05-evolution-of-mlplatform.md | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/_posts/2024-02-05-evolution-of-mlplatform.md b/_posts/2024-02-05-evolution-of-mlplatform.md index 089d8a1..37f22c2 100644 --- a/_posts/2024-02-05-evolution-of-mlplatform.md +++ b/_posts/2024-02-05-evolution-of-mlplatform.md @@ -86,31 +86,27 @@ MLOps is a methodology that provides a collection of concepts and workflows desi Scribd's ML Platform -- MLOps and Platforms in Action ------------------------------------- -At Scribd we have developed a machine learning platform which provides a curated developer experience for machine learning developers and applies the concepts of DevOps in the following ways +At Scribd we have developed a machine learning platform which provides a curated developer experience for machine learning developers. This platform has been built with MLOps in mind which can be seen through its use of common DevOps principles. -1. **Automation:** - +1. **Automation:** * Applying CI/CD strategies to model deployments through the use of Jenkins pipelines which deploy models from the Model Registry to AWS based endpoints. * Automating Model training throug the use of Airflow DAGS and allowing these DAGS to trigger the deployment pipelines to deploy a model once re-training has occured. 2. **Continuous** **Testing:** - * Applying continuous testing as part of a model deployment pipeline, removing the need for manual testing. * Increased tooling to support model validation testing. 3. **Monitoring:** - * Monitoring real time inference endpoints * Monitoring training DAGS + * Monitoring batch jobs 4. **Collaboration and Communication:** - * Feature Store which provides feature discovery and re-use * Model Database which provides model collaboration 6. **Version Control:** - - * Applyied version control to experiments, machine learning models and features + * Applying version control to experiments, machine learning models and features References