Version and track everything

Outerbounds was like Metaflow, but with a ‘plus plus’—it made everything easier.

I've been extremely impressed by the Metaflow team. They have built something that's loved by everyone, so much so that they have Metaflow sad hours to encourage people say what can be improved. In my conversations with our users, I've often heard that Metaflow is one of the best software Netflix has ever built.

I've been using Metaflow for years now. I can't endorse it nor the people behind it enough!

As an informal estimate, our #datascience team believes they were able to test twice as many models in Q1 2021 as they did in all of 2020, with simple experiments that would have taken a week now taking half a day.

MetaFlow has a really fantastic API.  Most DAG systems could learn from it.

Metaflow has really been helping in speeding up our ds workflows!

A space I'm excited about: 'open source on top of public cloud tech.' Instead of using open source libraries (which you have to run & scale, e.g. RabbitMQ), it uses infinitely-scalable off-the-shelf cloud services (e.g. SQS). Metaflow is a good example!

❤️ metaflow! The people-first API design is fantastic.

Metaflow records the result of every step to S3. IMO the ability of metaflow to expose individual components of a complicated process is a big deal. Like, a really big deal.

I really appreciate the clean syntax and the seamless transitioning from dev to dev-at-scale to prod-at-scale

One of the things I liked about Metaflow from Netflix is their clear documentation on "Dealing with Failures": it's important to raise awareness about "retry" logic, transient/permanent failures, idempotent operations, etc.

Built with the core idea of allowing ML engineers and data scientists to use Python as native way to work, this friendly approach has tremendously boosted Carsales productivity in ML

Metaflow has made my daily life as an ML engineer so much easier, especially as we scale our efforts. I've never worked with a framework that works out of the box as well as metaflow.

Been using metaflow for a while now, and love it. At its best, it hugely speeds up our development cycles

Metaflow has helped us avoid the anti-pattern of needing to push code to find out if something works

There's a lot of good ideas made practical in Metaflow to help ML projects and developers focus on what's important.

In six weeks, a team that hadn’t used Metaflow before was able to build an ML-based model, A/B test its performance, which handily beat the old simple approach, and roll it out to production.

Very impressive level of polish in the Metaflow docs/tutorials. Pretty incredible how many details have been abstracted away by Metaflow.

Thanks for Metaflow: it's a pleasure to use!

Metaflow was recommended at work. I spent some time with it today... it's amazing. Their tagline is on-point: "Build and manage real-life data science projects with ease."

We are saving over 90% of the @awscloud cost in our #artificialintelligence model training phase. We've just finished the automation of our AI pipelines through the implementation of @MetaflowOSS , and the migration from http://H2O.ai

The MLOps piece of tech I'm most excited about is @MetaflowOSS. Just from reading the docs I'm amazed at how a large, enterprise company like Netflix seems to have really, really grasped the developer experience in the way I'd only expect 'smaller' projects to do.

Wow I didn't realize how much of the infrastructure needs Metaflow takes care of!

The end-to-end ML management in Outerbounds—event triggers, experiment tracking, managed artifacts—was a game-changer for our small team.

Thanks for a fantastic product ❤️

Very impressive work! Easily mix multiple Python virtual environments and compute resources within a single pipeline 🤯 using #Metaflow

Bumped into Metaflow at work, plus @HamelHusain seems excited about it, so I am excited about it 🙂 But after watching the video, wow 😲

Metaflow is such an amazing tool!

Outerbounds has proven to be transformative for us. The platform has allowed us to execute and train models without the burden of worrying about infrastructure

By unifying our data and ML pipelines we can now automatically train models when an underlying warehouse table is updated which drastically improves efficiency and collaboration.

Maybe we'll be talking about this as the "Airflow for Data Science" in a few years. Not replacing Airflow, but in the sense that it's the default standard for orchestrating data science projects like Airflow does for data engineering pipelines.

The team has shaved months off the time it takes to build a productionized machine learning model, and at the same time improved the overall collaboration of the data science organization with other departments!

I love Metaflow!