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Based on our record, Metaflow should be more popular than Hamilton. 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.
Hey HN – Stefan and Elijah here from DAGWorks (http://dagworks.io/), we’re the authors of Hamilton (https://github.com/dagworks-inc/hamilton), an open-source library for building self-documenting, modular dataflows in python that works for data, ML, LLM pipelines, & even web-workflows. We’ve been developing this UI for a while and we’re excited to say we... - Source: Hacker News / about 1 month ago
In this post, we’ll show how your team can turn any utility function(s) into reusable IPython Jupyter magics for a better notebook experience. As an example, we’ll use Hamilton, my open source library, to motivate the creation of a magic that facilitates better development ergonomics for using it. You needn’t know what Hamilton is to understand this post. - Source: dev.to / 3 months ago
This is me. I drive an open source library Hamilton that people doing time-series/ML work love to use. I'm building a paid product around it at DAGWorks, and I'm after feedback on our current version. Can I entice anyone to:. Source: about 1 year ago
From a nuts and bolts perspective, I've been thinking of building some reactivity on top of https://github.com/dagworks-inc/hamilton (author here) that could get at this. (If you have a use case that could be documented, I'd appreciate it.). - Source: Hacker News / about 1 year ago
Otherwise, I'm biased here, but check out https://github.com/dagworks-inc/hamilton - it could be your universal layer that expresses how things should flow, that is orchestration system agnostic, which would make it easy to migrate between systems easily. Source: about 1 year 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: over 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
Observable - Interactive code examples/posts
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Django REST framework - Django REST framework is a toolkit for building web APIs.
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.