Top 8 alternatives to MLflow


There are different paradigms when it comes to the machine learning life cycle. It includes brainstorming, data collection, and exploratory data analysis, followed by R&D and validation, and finally provisioning and monitoring. As you try out different models and features, or when you update your training dataset, monitoring can periodically bring you back to the starting step. You can move all steps in the life cycle back to an earlier phase.

Platforms like MLflow have emerged as the first choice for many data scientists and ensure a smooth transition / experience in managing the machine learning lifecycle. It is currently one of the most popular open source platforms for managing the ML lifecycle. It includes experimentation, reproducibility, deployment and a centralized model registration.

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MLflow is currently used by companies such as Facebook, Databricks, Microsoft, Accenture and, among others. The platform is library-independent. It offers a number of simple APIs that can be used with any existing machine learning application or library such as TensorFlow, PyTorch, XGBoost, etc. It can run on notebooks, standalone applications, or the cloud.

MLflow currently has four functions:

  • MLflow tracking: Follows experiments to record and compare parameters and results.
  • MLflow projects: Wraps machine learning code in a reusable, reproducible form to share with other data scientists or to transfer to production.
  • MLflow models: Manages and exposes models from various machine learning libraries to a variety of model delivery and inference platforms.
  • MLflow model registration: Provides a central model repository for joint management of the entire life cycle of an MLflow model, including phase transitions, model versioning, and annotations.

This article explores the key alternatives to MLflow and explains their features and specifications that can help you and your team choose the right platform to manage your machine learning lifecycle.

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Neptune is a metadata store for MLOps. It’s a one-stop shop for logging, storing, viewing, organizing, comparing, and querying all of your model building metadata. The platform offers:

  • Experiment Tracking: Log, view, organize, and compare machine learning experiments
  • Model registration: versioning, saving, managing and querying trained models and model building metadata
  • Live monitoring of machine learning: record and monitor model training, evaluation or production runs live


Kubeflow makes the delivery of machine learning (ML) workflows on Kubernetes easy, portable and scalable. The platform offers an uncomplicated way to provide first-class open source systems for machine learning in various infrastructures. In other words, it’s an ML toolkit for Kubernetes.


target is an open source comparison tool for AI / ML experiments. The platform helps users compare thousands of training runs simultaneously using the framework-independent Python SDK and powerful user interface. In addition, it offers flexibility for:

  • Use multiple sessions in a training script to save multiple runs at the same time. It also creates a default session if it is not explicitly initialized.
  • Use experiments to group related runs – where else an experiment called “Standard” is created.
  • Use integrations to automate tracking.


comet provides a self-hosted and cloud-based meta-machine learning platform that enables data scientists to track, compare, explain and optimize experiments and models. Backed by users and Fortune 100 companies like Uber, Autodesk, Boeing, Hugging Face, AssemblyAI, and others, Comet provides data and insights to build better, more accurate AI / ML models while maintaining productivity, collaboration, and access team visibility to improve.

Guild AI

Guild AI is an open source platform for ML / AI experiment tracking, pipeline automation, and hyperparameter tuning. It offers several built-in tools, viz Guild comparison, Guild view, TensorBoard and Guild difference.

See also


souvenir is Open source version control for a machine learning library with Amazon S2 and Google Cloud Storage support. With souvenirs, you can

  • Track experiments: automatically track code, training data, hyperparameters, weights, metrics, etc.
  • Go back in time: you can get the code and weights back from each checkpoint if you need to replicate your results or commit to Git after the fact.
  • Version your models: model weights are stored in the Amazon S3 or Google Cloud bucket. This makes it easier for them to be fed into production systems.


ModelDB is a Versioning of open source ML models, Metadata and experiment management platform. ModelDB helps to make your ML models reproducible. It also helps you manage your ML experiments, create performance dashboards, and share reports. After all, it tracks models throughout their lifecycle, including development, deployment, and live monitoring.


Sacred is a tool to help you with the configuration, organize, record, reproduce experiments. The platform is designed to do all of the tedious overhead work you need to do around your actual experiments in order to –

  • Keep an eye on all parameters of your experiments
  • Just carry out your experiments for different settings / scenarios
  • Store configurations for a single run in a database
  • Reproduce results

All of this is achieved through the following mechanisms, including configuration scopes, configuration injection, command line interface, observer, and automatic seeding.

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Amit Raja Naik

Amit Raja Naik

Amit Raja Naik is Senior Writer at Analytics India Magazine, where he delves into the latest technological innovations. He is also a professional bass player.


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