Deeplearning4j VS Metaflow

Deeplearning4j Reviews

Metaflow Reviews

Deeplearning4j mentions (4)

• Machine Learning in Kotlin (Question)

  While KotlinDL seems to be a good solution by Jetbrains, I would personally stick to Java frameworks like DL4J for a better community support and likely more features. Source: over 2 years ago

• Does Java has similar project like this one in C#? (ml, data)

  Would recommend taking a look at dl4j: https://deeplearning4j.org. Source: almost 3 years ago

• just released my Clojure AI book

  We use DeepLearning4j in this chapter because it is written in Java and easy to use with Clojure. In a later chapter we will use the Clojure library libpython-clj to access other deep learning-based tools like the Hugging Face Transformer models for question answering systems as well as the spaCy Python library for NLP. Source: almost 3 years ago

• [D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS

  FastAPI. Or even simpler: DL4J, to be used in Java when we need to communicate with the rest of the applications in real time. Source: about 3 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 / almost 2 years ago