Machine Learning Ops Tools

In 2024, the MLOps domain is distinctly marked by the simultaneous presence of open-source and proprietary solutions. Open-source tools in MLOps have become increasingly popular for their adaptability, community-driven enhancements, and versatile application across different workflows. In this article, discover the best MLOps TOols of 2024.

11 Optimal Machine Learning Ops Tools: A Thorough Review

TensorFlow Extended

TensorFlow Extended (TFX) is an end-to-end platform designed to facilitate the deployment of production-ready machine learning models. It extends the capabilities of TensorFlow, Google's open-source machine learning library, by adding components necessary for the deployment, validation, and monitoring of models in a scalable and maintainable manner. See profile
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Key features

Integration with TensorFlow for model training

Tools for model validation and versioning

Pipeline orchestration with Apache Beam

MLflow

MLflow is an open-source platform focused on the lifecycle management of machine learning projects, including experimentation, reproducibility, and deployment. It provides a set of APIs and tools for managing the end-to-end machine learning lifecycle. See profile
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Key features

Experiment tracking

Model packaging and sharing

Model serving and deployment

Kubeflow

Kubeflow is a machine learning toolkit for Kubernetes, designed to make deployments of machine learning workflows on Kubernetes simple, portable, and scalable. It leverages Kubernetes' ability to orchestrate complex, distributed systems. See profile
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Key features

Kubernetes-native support

Scalable machine learning pipelines

Multi-framework support

Seldon Core

Seldon Core is an open-source platform that enables organizations to deploy, scale, and monitor machine learning models in Kubernetes environments. It focuses on the last mile of machine learning: model serving, scaling, monitoring, and governance. See profile
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Key features

Kubernetes-native ML model serving

Advanced model monitoring and explainability

Model governance and audit trails

DVC (Data Version Control)

DVC is an open-source version control system for machine learning projects. It extends Git's capabilities to handle large data files and machine learning models, allowing for better tracking of data, models, and experiments. See profile
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Key features

Data and model versioning

Reproducible machine learning pipelines

Integration with Git workflows

Pachyderm

Pachyderm is an open-source data science platform that combines data lineage with end-to-end machine learning pipelines on Kubernetes, providing reproducibility and automation in the ML lifecycle. See profile
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Key features

Data lineage and versioning

Reproducible ML pipelines

Kubernetes-native execution

Tecton

Tecton is a data platform for machine learning, providing an enterprise-ready feature store that enables teams to manage, serve, and monitor features for real-time machine learning applications at scale. See profile
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Plan
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Key features

Feature management for ML models

Real-time feature serving

Monitoring and governance of ML features

Metaflow

Metaflow is an open-source, human-centric framework for building and managing real-life data science projects. It was originally developed at Netflix to boost productivity in data science and engineering. See profile
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Key features

User-friendly Python library

Built-in data lineage and versioning

Scalable to cloud services

Weights & Biases

Weights & Biases provides tools for machine learning experiment tracking, model optimization, and dataset versioning, making it easier for teams to collaborate and produce reproducible research. See profile
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Key features

Experiment tracking and visualization

Hyperparameter tuning

Dataset and model versioning

Neptune.ai

Neptune.ai is a metadata store for MLOps, designed to help data scientists and ML engineers track, organize, and analyze their machine learning experiments and models. See profile
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Key features

Neptune.ai is a metadata store for MLOps, designed to help data scientists and ML engineers track, organize, and analyze their machine learning experiments and models.

Collaboration tools for ML projects

Model registry and management

Valohai

Valohai is a deep learning management platform that automates machine learning training and deployment pipelines, making it easier for teams to collaborate and manage ML projects at scale. See profile
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Pricing
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Plan
Quote Based
Key features

Automated ML pipelines

Scalable machine learning training and deployment

Reproducible environments and version control