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An open source machine learning platform
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What is MLflow?

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
MLflow is a tool in the Machine Learning Tools category of a tech stack.
MLflow is an open source tool with 17.4K GitHub stars and 4K GitHub forks. Here’s a link to MLflow's open source repository on GitHub

Who uses MLflow?

32 companies reportedly use MLflow in their tech stacks, including Hepsiburada, Peloton, and

164 developers on StackShare have stated that they use MLflow.

MLflow Integrations

Databricks, Neptune, DAGsHub, Flyte, and Truss are some of the popular tools that integrate with MLflow. Here's a list of all 5 tools that integrate with MLflow.
Pros of MLflow
Code First
Simplified Logging
Decisions about MLflow

Here are some stack decisions, common use cases and reviews by companies and developers who chose MLflow 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

I already use DVC to keep track and store my datasets in my machine learning pipeline. I have also started to use MLflow to keep track of my experiments. However, I still don't know whether to use DVC for my model files or I use the MLflow artifact store for this purpose. Or maybe these two serve different purposes, and it may be good to do both! Can anyone help, please?

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MLflow's Features

  • Track experiments to record and compare parameters and results
  • Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
  • Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms

MLflow Alternatives & Comparisons

What are some alternatives to MLflow?
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.
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.
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.
It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
Seldon is an Open Predictive Platform that currently allows recommendations to be generated based on structured historical data. It has a variety of algorithms to produce these recommendations and can report a variety of statistics.
See all alternatives

MLflow's Followers
511 developers follow MLflow to keep up with related blogs and decisions.