Scalable Compute

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.

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

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

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

The developer experience is great; we love how fast we can iterate, and the code looks clean—it’s just Python.

I love Metaflow!

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

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

Outerbounds enables us to focus on innovation, not infrastructure. They handle the complexity, so we can concentrate on delivering real business impact.

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

We’ve gone from manually tracking artifacts in S3 with naming conventions to essentially zero overhead in that area thanks to Metaflow's artifact management.

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

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 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.

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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'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.

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

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

Thanks for a fantastic product ❤️

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 lets us test hypotheses faster than ever—speed is critical in AI, and Metaflow delivers

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."

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

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.

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.

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

Reproducibility and compliance were critical for us in the lending industry, and Outerbounds delivered that seamlessly!

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

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.

With Outerbounds, the team has shaved months off the time it takes to ship a productionized machine learning model!

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

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

Metaflow has been a fantastic tool for Carsales. Built with the core idea of allowing ML engineers and data scientists to use Python as a native way to work, this friendly approach has tremendously boosted Carsales' productivity in ML related projects.

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Metaflow is such an amazing tool!

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

Metaflow turned a three-day manual process into a few hours of automated execution for our most critical models.

We went from a six-month deployment process to just two weeks with Metaflow - drastically improving our ability to deliver healthcare innovations!

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

After adopting Metaflow, we were able to ship eight additional models in just ten months, whereas before it took many more months to launch a single model!

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

With Outerbounds, we can scale across thousands of machines and get tasks done in hours instead of days - an absolute game changer!

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!

What stood out about Outerbounds was their responsiveness. When we asked for features, they delivered exactly what we needed and fast.

With Metaflow, we were able to test twice as many models in a single quarter as we did in all of previous year!

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.