What is Metaflow?
It is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. It was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
Metaflow is a tool in the Data Science Tools category of a tech stack.
Metaflow is an open source tool with 3.3K GitHub stars and 247 GitHub forks. Here’s a link to Metaflow's open source repository on GitHub
Who uses Metaflow?
Pros of Metaflow
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- End-to-end ML Platform
- Model with your favorite tools
- Powered by the AWS cloud
- Battle-hardened at Netflix
Metaflow Alternatives & Comparisons
What are some alternatives to Metaflow?
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Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.