Based on our record, PyTorch should be more popular than Metaflow. It has been mentiond 106 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.
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more... - Source: dev.to / about 1 month ago
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch. - Source: dev.to / 2 months ago
Import torch # we use PyTorch: https://pytorch.org Data = torch.tensor(encode(text), dtype=torch.long) Print(data.shape, data.dtype) Print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this. - Source: dev.to / 3 months ago
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries. - Source: dev.to / 2 months ago
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework. - Source: dev.to / 2 months 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
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
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...