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The Three Virtues of MLOps: Velocity, Validation and Versioning

Sandra YoussefSandra Youssef
Jan 9, 202314 min read

UC Berkeley Ph.D. candidate Rolando Garcia recently presented the findings of his team’s “Operationalizing Machine Learning: An Interview Study”...

The Three Virtues of MLOps: Velocity, Validation and Versioning

How Flyte is Simplifying the Orchestration of Machine Learning Pipelines

Deactivated UserDeactivated User
Sep 21, 20225 min read

Introducing a suite of new ML add-ons · An ML-powered product comprises several pieces composed into an iterative cycle — data collection, data cleaning,...

How Flyte is Simplifying the Orchestration of Machine Learning Pipelines

Ray and Flyte: Distributed Computing and Orchestration

Kevin SuKevin Su
Aug 25, 20225 min read

Whether it is asking, “Why do 87% of data science projects never make it into production?” or trying to make sense of a “hot mess” called MLOps,...

Ray and Flyte: Distributed Computing and Orchestration

Scale Airflow for Machine Learning Tasks with the Flyte Airflow Provider

Deactivated UserDeactivated User
Jul 7, 20225 min read

Apache Airflow is an open-source platform that can be used to author, monitor, and schedule data pipelines. It is used by companies like Airbnb, Lyft,...

Scale Airflow for Machine Learning Tasks with the Flyte Airflow Provider

Build an Event-Driven Neural Style Transfer Application Using AWS Lambda

Deactivated UserDeactivated User
May 3, 202211 min read

To build a production-ready ML application and ensure its stability in the long run, we need to take care of a long checklist of requirements which...

Build an Event-Driven Neural Style Transfer Application Using AWS Lambda

Data-Parallel Distributed Training With Horovod and Flyte

Deactivated UserDeactivated User
Dec 8, 20217 min read

This blog is with reference to a talk titled “Efficient Data Parallel Distributed Training with Flyte, Spark & Horovod”, presented by Katrina Rogan...

Data-Parallel Distributed Training With Horovod and Flyte