Flyte
The workflow automation platform for complex, mission-critical data and ML processes at scale. Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing. Flyte is used in production at Lyft, Spotify, Freenome, and others. At Lyft, Flyte has been serving production model training and data processing for over four years, becoming the de-facto platform for teams like pricing, locations, ETA, mapping, autonomous, and more. In fact, Flyte manages over 10,000 unique workflows at Lyft, totaling over 1,000,000 executions every month, 20 million tasks, and 40 million containers. Flyte has been battle-tested at Lyft, Spotify, Freenome, and others. It is entirely open-source with an Apache 2.0 license under the Linux Foundation with a cross-industry overseeing committee. Configuring machine learning and data workflows can get complex and error-prone with YAML.
Learn more
Union Cloud
Union.ai is an award-winning, Flyte-based data and ML orchestrator for scalable, reproducible ML pipelines. With Union.ai, you can write your code locally and easily deploy pipelines to remote Kubernetes clusters.
“Flyte’s scalability, data lineage, and caching capabilities enable us to train hundreds of models on petabytes of geospatial data, giving us an edge in our business.”
— Arno, CTO at Blackshark.ai
“With Flyte, we want to give the power back to biologists. We want to stand up something that they can play around with different parameters for their models because not every … parameter is fixed. We want to make sure we are giving them the power to run the analyses.”
— Krishna Yeramsetty, Principal Data Scientist at Infinome
“Flyte plays a vital role as a key component of Gojek's ML Platform by providing exactly that."
— Pradithya Aria Pura, Principal Engineer at Goj
Learn more
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.
Learn more
Apache Mahout
Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets. Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends. Matrix computations are a fundamental part of many scientific and engineering applications, including machine learning, computer vision, and data analysis. Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark.
Learn more