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21 open source MLOps tools and their key capabilities

Over the past few years, machine learning has moved from experimental labs to mainstream business operations. Organizations across every industry — from manufacturing and finance to healthcare and retail — are now using ML to optimize processes, enhance customer experiences, detect fraud, strengthen security, and even support medical diagnosis and treatment.

MLOps open source tools

As this adoption accelerates, so does the need to manage the growing complexity of ML workflows. Building, deploying, and maintaining models at scale requires consistent processes, collaboration, and automation — collectively known as MLOps.

MLOps brings together data engineering, model development, deployment, and monitoring under one framework. Managing these interconnected tasks manually is nearly impossible, which is why a rich ecosystem of open-source and commercial MLOps tools has emerged to make the process more efficient and transparent.

In this article, we explore 21 of the most popular open-source MLOps tools available today — what they do, where they shine, and how they help data scientists and ML engineers streamline their workflows. For clarity, we’ve grouped them into categories based on their core capabilities, so you can easily identify which solutions fit your needs and maturity level.

Workflow Management Tools

Workflow management tools help MLOps and ML engineering teams organize, automate, and scale the complex processes behind machine learning model development and deployment. They combine features such as pipeline orchestration, version control, and experiment tracking, enabling collaboration, reproducibility, and faster iterations across teams.

  1. Kubeflow is a Kubernetes-native platform for running ML workflows — including model training, hyperparameter tuning, and model serving. It simplifies building, deploying, and managing machine learning pipelines on Kubernetes clusters, allowing teams to leverage containerized infrastructure for scalability and performance.
  2. MLflow offers an end-to-end approach to managing the ML lifecycle, from data preparation to production deployment. It allows data scientists to track and visualize experiments, package reproducible runs, manage model versions, and integrate seamlessly with popular ML frameworks.
  3. Kiroframe is an MLOps platform designed to track, automate, and scale AI development. It enables teams to manage experiments, datasets, and shared environments from a single interface — ensuring reproducibility, transparency, and collaboration throughout the ML lifecycle. With features like model profiling, dataset versioning, and leaderboard comparisons, Kiroframe helps teams accelerate development, identify performance bottlenecks, and standardize ML workflows across projects.
  4. Metaflow provides a high-level abstraction layer for building and managing end-to-end machine learning workflows. It handles infrastructure components such as data storage, execution, and monitoring, while supporting experiment tracking and version control. Metaflow integrates smoothly with Python libraries like Pandas, NumPy, and TensorFlow, making it ideal for production-ready ML pipelines.
  5. Kedro is an open-source Python framework that simplifies the creation of modular, reusable, and reproducible ML pipelines. It supports every stage — from data preprocessing and model training to deployment — with built-in data versioning and dependency management. Kedro’s project templates promote consistent structure and maintainability in large-scale ML initiatives.
  6. ZenML provides modular pipelines that simplify the orchestration of data preprocessing, model training, and deployment. It integrates easily with leading ML frameworks and cloud providers, offering flexibility for teams looking to streamline production workflows while maintaining transparency and auditability.
  7. MLReef is a collaborative platform designed to unify ML project development. It offers integrated tools for version control, data management, and model deployment, making it easier for distributed teams to co-develop and share components across projects. Its compatibility with various frameworks enhances adaptability for multi-team ML environments.
  8. MLRun automates the creation and execution of ML pipelines. It coordinates data ingestion, preprocessing, training, and deployment tasks across multiple frameworks, helping teams improve workflow efficiency. Its collaboration features allow data scientists and developers to co-author projects, optimize training pipelines, and track results with minimal manual effort.
  9. CML (Continuous Machine Learning) brings CI/CD principles to ML workflows. It automates data ingestion, experiment testing, and model deployment, ensuring continuous integration and delivery of ML models. CML accelerates iteration speed and reduces operational overhead in development pipelines.
  10. Cortex Lab simplifies deploying ML models at scale, handling automatic scaling, monitoring, and alerting. It integrates seamlessly with cloud infrastructure and supports a variety of ML frameworks, ensuring that models run efficiently and reliably in production environments.

Automated Machine Learning tools

Automated Machine Learning (AutoML) tools simplify and accelerate the most time-consuming parts of the machine learning lifecycle — such as model selection, hyperparameter tuning, and feature engineering. By automating these steps, data scientists and MLOps engineers can focus on higher-level tasks like model interpretation, evaluation, and deployment.

Many AutoML platforms use advanced techniques such as neural architecture search, ensemble learning, and reinforcement learning to optimize model performance with minimal manual intervention. These tools not only improve accuracy but also make machine learning more accessible to non-experts, reducing the barrier to entry for experimentation and production.

  1. AutoKeras is an open-source library that automates the creation and deployment of machine learning models. It applies neural architecture search algorithms to identify the best architecture for each dataset and task, automatically handling hyperparameter tuning and data preprocessing for classification, regression, and image or text analysis. AutoKeras makes it possible to build accurate models quickly without in-depth ML expertise.
  2. H2O AutoML streamlines the process of building, training, and optimizing machine learning models. It supports a wide range of use cases — from predictive analytics to data classification — by automatically selecting algorithms, optimizing parameters, and producing ready-to-deploy models. H2O AutoML is particularly popular in both educational and enterprise settings, allowing users to experiment with robust ML workflows without manual fine-tuning.
  3. NNI (Neural Network Intelligence) is a toolkit designed to automate hyperparameter optimization and model architecture tuning. It systematically explores different parameter configurations to identify the most effective model setup, minimizing human error and time investment. NNI integrates with popular frameworks like TensorFlow, PyTorch, and Scikit-learn, making it a powerful option for scalable experimentation and performance improvement.

Big Data Processing, Labeling, and Version Control Tools

Big Data Processing tools are essential for managing, processing, and analyzing the massive volumes of data that fuel machine learning systems. They handle everything from data ingestion and transformation to labeling, version control, and storage, ensuring that teams can efficiently work with complex, large-scale datasets while maintaining consistency and traceability.

  1. Hadoop is one of the most established platforms for distributed data processing. It allows organizations to store and analyze enormous datasets across clusters of machines using the Hadoop Distributed File System (HDFS). Its MapReduce processing model enables parallel data computation, which dramatically improves performance and scalability when handling big data workloads.
  2. Apache Spark is a high-performance data processing framework built for speed and flexibility. It supports distributed computing and enables fast, large-scale data analytics through in-memory processing. Spark works with multiple programming languages — including Java, Python, and Scala — and includes a rich ecosystem of libraries for streaming, machine learning, and graph analytics.
  3. Data Version Control (DVC) provides versioning capabilities for datasets, machine learning models, and experiments. It works seamlessly with Git, allowing teams to track changes, reproduce results, and roll back to previous versions when necessary. By combining data management and collaboration features, DVC enhances reproducibility and transparency in ML projects.
  4. Pachyderm offers a Git-like approach to data pipeline management. It allows users to create reproducible data workflows with automatic version control for every dataset and transformation step. Pachyderm’s focus on lineage tracking helps teams maintain accountability and ensures that every output can be traced back to its source data and process.
  5. Label Studio simplifies the process of data labeling — one of the most crucial steps in supervised learning. With a user-friendly web interface, it supports multiple data types, including images, text, audio, and video. Label Studio helps teams annotate large datasets collaboratively, ensuring data quality and consistency before training ML models.
What are the main challenges of the MLOps process? Read more →
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Model Deployment and Serving

  1. Model deployment and serving tools bridge the gap between experimentation and real-world application. They are built to deploy trained ML models into production environments and ensure those models deliver accurate, reliable predictions to end users. These tools commonly include scaling, monitoring, and observability features to keep production systems stable and performant.
  2. Seldon Core is a powerful, open-source platform for deploying and serving machine learning models on Kubernetes and major cloud environments. It enables packaging ML models as microservices, which simplifies integration with other applications and APIs. Seldon Core supports automated scaling, performance monitoring, and advanced metrics tracking, helping teams manage multiple models efficiently and maintain production-grade reliability.
  3. Flyte is an open-source orchestration platform for building, executing, and managing complex machine learning and data workflows. It provides built-in capabilities for tracking performance metrics, logs, and workflow visualization, making it easier to monitor pipelines from training to deployment. Flyte helps teams automate end-to-end ML processes with reproducibility and scalability in mind.
  4. Jina is an open-source framework for building and deploying neural search and multimodal AI applications. It allows developers to design and scale deep-learning-powered search systems across text, images, audio, or video data. Jina offers robust deployment options on Kubernetes, along with monitoring, logging, and autoscaling features to ensure production-ready performance for large-scale AI services.

A few words about Kiroframe

We wanted to give a special mention to Kiroframe, a modern SaaS MLOps platform built to streamline every stage of machine learning and AI development. Kiroframe brings together experiment tracking, dataset management, shared environment scheduling, and model profiling — all within a unified, cloud-based workspace designed for ML engineers, data scientists, and research teams.

The platform helps organizations track, automate, and scale ML workflows effortlessly. With version-controlled datasets, experiment history, and reproducible pipelines, teams can move from idea to deployment faster while maintaining full visibility into results. Kiroframe’s flexible SaaS architecture makes it easy to collaborate securely across distributed teams without worrying about infrastructure setup or maintenance.

Some of Kiroframe’s key capabilities include:

  • Dataset tracking and versioning – link datasets to experiments, track usage, and ensure full transparency across ML projects.

  • Experiment management and leaderboards – organize, compare, and visualize results to identify top-performing models faster.

  • Model profiling – detect bottlenecks, analyze performance metrics, and optimize resource efficiency.

  • Shared environment control – manage and schedule compute environments, reducing idle time and conflicts across ML workloads.

  • Workflow automation – streamline repetitive tasks and integrate with popular ML frameworks for seamless scaling.

Unlike standalone open-source tools, Kiroframe delivers these capabilities as a fully managed SaaS solution, combining ease of use with enterprise-grade scalability and security.

To sum up, while many open-source MLOps tools cover specific parts of the ML lifecycle — from workflow management to model deployment — Kiroframe offers a comprehensive, ready-to-use environment for collaborative, transparent, and efficient AI development. It’s the perfect choice for teams seeking to optimize performance, accelerate innovation, and maintain full control over their ML operations in one intuitive platform.