Dask VS Metaflow

Dask Reviews

Metaflow Reviews

Dask mentions (16)

• Large Scale Hydrology: Geocomputational tools that you use

  We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk. Source: about 2 years ago

• msgspec - a fast & friendly JSON/MessagePack library

  I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec. Source: about 2 years ago

• What does it mean to scale your python powered pipeline?

  Dask: Distributed data frames, machine learning and more. - Source: dev.to / over 2 years ago

• Data pipelines with Luigi

  To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:. - Source: dev.to / over 2 years ago

• How to load 85.6 GB of XML data into a dataframe

  I’m quite sure dask helps and has a pandas like api though will use disk and not just RAM. Source: over 2 years ago

Metaflow mentions (12)

• What are some open-source ML pipeline managers that are easy to use?

  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

• Needs advice for choosing tools for my team. We use AWS.

  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

• Selfhosted chatGPT with local contente

  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

• [OC] Gender diversity in Tech companies

  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

• Going to Production with Github Actions, Metaflow and AWS SageMaker

  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 / over 1 year ago