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What is Kubeflow?

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.
Kubeflow is a tool in the Machine Learning Tools category of a tech stack.
Kubeflow is an open source tool with GitHub stars and GitHub forks. Here’s a link to Kubeflow's open source repository on GitHub

Who uses Kubeflow?

36 companies reportedly use Kubeflow in their tech stacks, including Hepsiburada, BlaBlaCar, and Beat.

159 developers on StackShare have stated that they use Kubeflow.

Kubeflow Integrations

Kubernetes, TensorFlow, Jupyter, Google AI Platform, and Pipelines are some of the popular tools that integrate with Kubeflow. Here's a list of all 9 tools that integrate with Kubeflow.
Pros of Kubeflow
System designer
Google backed
Kfp dsl
Decisions about Kubeflow

Here are some stack decisions, common use cases and reviews by companies and developers who chose Kubeflow in their tech stack.

Needs advice

We are trying to standardise DevOps across both ML (model selection and deployment) and regular software. Want to minimise the number of tools we have to learn. Also want a scalable solution which is easy enough to start small - eg. on a powerful laptop and eventually be deployed at scale. MLflow vs Kubernetes (Kubeflow)?

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Biswajit Pathak
Project Manager at Sony · | 6 upvotes · 845.4K views
Needs advice

Can you please advise which one to choose FastText Or Gensim, in terms of:

  1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
  2. Performance
  3. Customization of Intermediate steps
  4. FastText and Gensim both have the same underlying libraries
  5. Use cases each one tries to solve
  6. Unsupervised Vs Supervised dimensions
  7. Ease of Use.

Please mention any other points that I may have missed here.

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Needs advice
Amazon SageMakerAmazon SageMaker

Amazon SageMaker constricts the use of their own mxnet package and does not offer a strong Kubernetes backbone. At the same time, Kubeflow is still quite buggy and cumbersome to use. Which tool is a better pick for MLOps pipelines (both from the perspective of scalability and depth)?

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Blog Posts


Kubeflow Alternatives & Comparisons

What are some alternatives to Kubeflow?
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.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
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.
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
See all alternatives

Kubeflow's Followers
579 developers follow Kubeflow to keep up with related blogs and decisions.