Based on our record, Metaflow should be more popular than Porter. It has been mentiond 12 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Https://getporter.org/ https://getporter.dev/ One of you is going to have to rename yourselves... - Source: Hacker News / over 1 year ago
Porter - a fully-managed PaaS that lets teams automate DevOps. The free basic tier for porter cloud offers management of 1 cluster with up to 10 vCPU and 20 GB memory. - Source: dev.to / over 1 year ago
There are some YC startups (AtomizedHq.com and getporter.dev) that are doing really interesting things with cross-cloud K8S deployments (more like heroku). These are all different bits of the serverless microservices scaling puzzle. We are a long way off but trying to think long term, even as a 2 person alpha prototype :). Source: almost 3 years ago
But then I saw a YC startup called Porter (https://getporter.dev) that made getting the cluster set up and deploying the apps from Heroku on AWS EKS a piece of cake. It's really great. There is another YC startup called Atomized (https://atomizedhq.com) that I've been looking at that's also really great. They are both worth checking out, and the teams from both are super-responsive. Source: almost 3 years ago
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: about 1 year ago
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling. Source: about 1 year ago
Even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf. Source: about 1 year ago
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural... Source: over 1 year ago
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project. - Source: dev.to / almost 2 years ago
Heroku - Agile deployment platform for Ruby, Node.js, Clojure, Java, Python, and Scala. Setup takes only minutes and deploys are instant through git. Leave tedious server maintenance to Heroku and focus on your code.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Render UIKit - React-inspired Swift library for writing UIKit UIs
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.
DepHell - :package: :fire: Python project management. Manage packages: convert between formats, lock, install, resolve, isolate, test, build graph, show outdated, audit. Manage venvs, build package, bump ver...