Best 10 Open-source MLOps Tools to Optimize & Manage ML

Do you need help managing and optimizing your Machine Learning workflows? You’re not alone. In today’s rapidly evolving AI landscape, selecting the right MLOps tools is crucial for success. Picture this: a fellow data scientist, overwhelmed by the complexity of their ML project, discovers a set of open-source tools that transform their workflow, boosting efficiency and performance. Intrigued? You should be. This guide will introduce you to the top 10 open-source MLOps tools that can help streamline your ML projects and keep you ahead of the curve. Advancement in Machine Learning and Data Science demands efficient handling of ML models while improving productivity and collaboration. With that regard, Open-source MLOps tools are gaining popularity among data scientists and engineers. These transformative tools have the potential to reshape your work processes and include Deepchecks, MLflow, DVC, ZenML, Kubeflow, Metaflow, Kedro, Seldon Core, Pachyderm, and Ray. Implementing an open-source MLOps tool can significantly boost the development process of ML models while saving time and enhancing productivity. Let’s embark on an adventure to uncover the vast landscape of open-source MLOps tools and unlock their full potential.

Efficient management and optimization of workflows are crucial for Machine Learning and AI growth. MLOps tools enable streamlined workflow and collaboration, ensure reproducibility and version control, monitor model performance and resource utilization, and automate deployment and scaling. Without MLOps tools, the ML lifecycle becomes cumbersome, and teams spend hours on non-value-add tasks, leading to governance and compliance issues. Adopting MLOps tools in ML and AI is no longer a ‘nice-to-have’, but a ‘must-have’. In a constantly evolving technological landscape.

This article explores the top 10 open-source MLOps tools that offer diverse capabilities and features to cater to various needs in the ML landscape. These tools provide scalability, seamless integrations with popular ML frameworks, and flexibility to revolutionize the approach to Machine Learning and AI projects. The article aims to enable organizations to stay competitive and deliver better business outcomes. Let’s dive into the world of open-source MLOps tools and take your ML game to the next level!

Deepchecks is a powerful Python package designed to facilitate effortless and thorough validation of ML models and data. It offers pre-defined checks and suites for model evaluation, data integrity, train-test validation, and the capability to create custom checks tailored to specific requirements. As an open-source MLOps tool, Deepchecks aids in identifying and mitigating model quality concerns, ensuring model accuracy, dependability, and currentness with an array of testing, validation, and monitoring tools.

DeepCheck Validation, Source: Deepcheck

Deepchecks offers the following core features:

• Automated model validation: Comprehensive checks on model performance, data integrity, and fairness.
• Customizable checks: Tailored validation process to specific project requirements.
• Model monitoring: Continuous tracking of model performance in production, detecting potential issues, and ensuring effectiveness over time.
• One-stop solution: Combination of model validation, monitoring, and explainability in a single, user-friendly platform.

Deepchecks offers the Deepchecks Hub platform, built on an open-source testing product, for continuous model and data validation and monitoring throughout their lifecycle.

Deepchecks offers not only an open-source project but also the Deepchecks Hub, a platform that enables ML practitioners to continuously validate and monitor their models and data throughout their entire lifecycle. It is built on an open-source testing product.   Key features of Deepchecks Hub include seamless validation checks transition from research to production, code-level root cause analysis, scalability for handling multiple models with large datasets, and real-time model performance monitoring and issue resolution. Deepchecks Hub works within existing ML, DevOps, and IT solutions, with security measures like Single Sign-On, data encryption, data separation, and secure SDK.

Deepchecks offers extensive documentation, blogs, glossary, events, and slack community to support users in effectively leveraging its capabilities. Deepchecks is an open-source tool, and users can access its features and source code for free. Commercial offerings with additional support and features are also available.

MLflow is an open-source MLOps platform developed by Databricks, designed to simplify the management of Machine Learning workflows. It offers a comprehensive tool suite that enables data scientists and Machine Learning engineers to manage the entire (end-to-end) ML workflow. MLflow’s primary goal is to standardize the process and increase reproducibility, collaboration, and efficiency in ML projects by tracking experiments, packaging code, and deploying models to various environments.

It provides four main components:

• Tracking: Log experiments and metrics, and compare and evaluate models.
• Projects: Organize and package code and dependencies, and simplify experiment reproduction.
• Models: Deploy and manage models in different environments and version models for easy deployment.
• Registry: Store and manage models in a centralized location and share models with team members.

MLflow supports Python, R, and Java, making it accessible to various data science and ML teams. It can also be integrated with cloud services like AWS SageMaker, Azure ML, and Databricks for scalable cloud-based ML workflows. MLflow has a thriving community of contributors and users driven by its open-source nature. The community provides support and feature updates and addresses issues through GitHub, mailing lists, slack, and documentation. MLFLow is available under the Apache License 2.0; it allows free use, modification, and distribution.

Data Version Control (DVC) is an open-source version control system that focuses on managing data and Machine Learning models. It focuses on versioning and tracking large data files, model artifacts, and experiments, facilitating collaboration and reproducibility in ML projects. DVC works with popular version control systems like Git, making integrating with existing codebases and workflows easy. It was designed to efficiently manage large-scale data sets and workflows.

DVC offers the following features:

• Data Versioning: Version control of large datasets and binary files in a Git-like manner, ensuring a clear record of changes and facilitating collaboration.
• Experiment Management: Simplifies organizing and tracking experiments, making comparing and selecting the best-performing model version easy.
• Data Pipelines: Enables reproducible data pipelines that automate the stages of an ML project, from data processing to model evaluation.
• Data Storage Management: Integrates with cloud storage providers like AWS S3, Google Cloud Storage, and Azure Blob Storage, making it easy to store and share large datasets.
• Metrics Tracking: Tracks model performance metrics, enabling easy comparison of results across different experiments and model versions.

DVC matches the right versions of data, code, and models Source: DVC

DVC seamlessly integrates with Git for version control and is compatible with various data science tools. It works independently of programming languages, supporting Python, R, Julia, and others. Additionally, DVC’s compatibility with cloud storage providers (Amazon S3, Google Cloud Storage, and Microsoft Azure) and remote storage solutions ensure data storage and sharing flexibility. DVC also provides a range of command-line tools that enable users to perform tasks such as versioning data sets, tracking model changes, and reproducing experiments. DVC boasts an active community of users and contributors who provide support, share experiences, and contribute to the development of the tool. The community offers resources such as use cases, documentation, blogs, courses, and community engagements on GitHub, events, and online forums like Discord. This ensures the tool stays up-to-date and relevant in the evolving ML landscape.

ZenML simplifies data, model, and pipeline management, allowing for easy development, deployment, and maintenance of ML applications. Developed by Maiot, it offers a comprehensive suite of tools for managing and scaling ML workflows. ZenML automates the end-to-end process of developing, deploying, and managing Machine Learning pipelines, from data collection to model deployment. It provides performance optimization tools to enhance model performance and reduce training times. ZenML simplifies complex ML pipelines by enabling end-to-end management of the Machine Learning pipeline, prioritizing automation, data management, deployment flexibility, reproducibility, and collaboration. It provides performance optimization tools to enhance model performance and reduce training times.

Some of its features include:

• Write Local, Run Anywhere: Develop and execute ML workflow code locally in various environments, supporting both on-premises and cloud-based deployment.
• Automatic Caching: Intelligently caches unchanged artifacts, conserving compute resources and time.
• Data Versioning: Automatically versions and tracks data flowing between pipeline steps, ensuring clear data lineage.
• Metadata Tracking: Automatically tracks all ML metadata, providing complete lineage and reproducibility for ML workflows.
• Customization: Extend ZenML to meet specific workflow requirements when existing features don’t suffice.
• Collaboration: Enables seamless collaboration among ML practitioners, from data scientists to ML engineers.
• Visualization: Utilize pre-built visualizers to analyze pipeline results with popular libraries like Dash, Plotly, and Facets.
• Continuous Training and Deployment (CT/CD): Supports continuous training and deployment of ML models, ensuring up-to-date and accurate models.

ZenML architecture, Source ZenML

ZenML’s compatibility with cloud storage providers and remote storage solutions ensures data storage and sharing flexibility. See the full list here, cutting across cloud infrastructure, data annotator, data validator, experiment tracking, feature store, modeling, orchestrator, and image builder. The ZenML community consists of users and contributors who actively engage in the development and support of the tool. ZenML provides projects, documentation, blogs, podcasts, newsletter, and online forums like Slack to foster community engagements to stay up-to-date and relevant in the constantly evolving ML landscape.

ZenML is available under the Apache License, Version 2.0, enabling users to use, modify, and distribute the software free of charge. In addition to its free and open-source version, ZenML also offers a paid enterprise version that includes all the features of the open-source framework, along with several additional benefits. These include collaboration features like the ability to share steps and reports, hosted cloud backends for an organization’s infrastructure, role-based access control, LDAP and SSO support, managed on-prem or cloud ZenML deployment with SLA, dedicated priority support, custom integration development as per project requirements, and onboarding sessions for teams. This enterprise version is tailored to meet the needs of organizations that require advanced capabilities and support.

Kubeflow is an open-source platform designed to simplify the deployment of Machine Learning workflows on Kubernetes. Kubeflow is an open-source MLOps platform designed to simplify the deployment, scaling, and management of Machine Learning workflows on Kubernetes. Kubeflow builds on top of Kubernetes. It aims to facilitate the end-to-end process of building, training, and deploying ML models by providing a consistent, reproducible, and scalable environment. It offers a range of Machine Learning-specific tools and components that enable users to build and deploy Machine Learning applications that are scalable and efficient.

Some of the critical features of Kubeflow include the following:

• Kubernetes Integration: Kubeflow is built on top of Kubernetes, leveraging its orchestration capabilities to manage complex ML workflows and ensure efficient resource utilization.
• Modular Design: Kubeflow offers a set of modular components that can be combined and customized to create tailored ML workflows and pipelines.
• Model Serving: Kubeflow provides tools for serving ML models, enabling users to deploy and manage models in a production environment easily.
• Portable Workflows and Deployment: Kubeflow supports portable workflows and deployment on any Kubernetes cluster, allowing users to deploy Machine Learning models on various environments, from local servers to large-scale cloud platforms, while promoting collaboration and reducing configuration errors.
• Version control: Kubeflow offers data and model version control, allowing users to track changes in pipelines and configurations.
• Performance optimization: Kubeflow provides tools for experiment tracking, hyperparameter tuning, distributed training, streamlining model development and evaluation, and optimizing model performance while reducing training time and resources.

Kubeflow architecture, Source Kubeflow

Kubeflow integrates seamlessly with Kubernetes and is compatible with cloud-native technologies, such as containers and microservices, ensuring flexibility and adaptability in various environments. Kubeflow can also be integrated with popular cloud platforms like AWS, Google Cloud, and Azure for scalable and managed ML workflows.  Kubeflow fosters an active and engaged community through its official mailing list, weekly Zoom calls, blog, and community-curated list of projects and resources. Working groups maintain specific aspects of the platform, enabling collaboration and continuous improvement.

Testing. CI/CD. Monitoring.

Because ML systems are more fragile than you think. All based on our open-source core.

Deepchecks HubOpen Source

Metaflow is an open-source MLOps tool created by Netflix, designed to streamline the construction and management of data science workflows and projects. Its primary objective is to simplify the development, deployment, and scaling of data-intensive applications, enabling data scientists and engineers to concentrate on solving the core problems. Focused on addressing data scientists’ practical challenges, Metaflow is built on real-life, business-oriented ML use cases. It manages complexity using Python or R and prioritizes usability, reducing cognitive overhead. Metaflow promotes collaboration and offers seamless reproducibility, enabling easy access to previous results. It supports both prototyping and production, emphasizing scalability and practical data access. By providing tools for proactive monitoring and reactive debugging, Metaflow minimizes operational issues and costs, all while following a human-centric design approach to optimize data scientist productivity.

Metaflow supports prototyping and production, focusing on straightforward scalability and a pragmatic approach to data access and processing. It provides specific data tooling for larger datasets and uses proven paradigms for data processing. Metaflow is designed for failure cases and provides tools for proactive monitoring and reactive debugging to minimize operational issues and costs. Metaflow’s philosophy is centered around human-centric design to optimize data scientist productivit

Some of its core features include:

• Human-centric Design: Metaflow is designed to be easy to learn and use, prioritizing the user experience to enable rapid prototyping and iteration. It also provides an intuitive and easy-to-read API.
• Versioning: It automatically versions code, data, and models, enabling users to easily track, reproduce, and iterate on their experiments.
• AWS Integration: Metaflow natively integrates with Amazon Web Services (AWS), allowing users to leverage cloud-based resources and services for data storage, computing, and deployment. Also, etaflow can be integrated with AWS Step Functions, enabling users to create and manage complex ML workflows using a visual interface.
• Metadata Management: Metaflow provides a built-in metadata store for tracking and managing ML workflow metadata, improving reproducibility and collaboration.
• Resource management: It includes built-in support for parallelization, allowing users to speed up their experiments and make better use of available computing resources.

Metaflow Architecture, Source Metaflow

Metaflow can be easily extended and customized using Python, allowing users to integrate it with their existing toolsets and workflows. The Metaflow community is actively engaged in developing, supporting, and promoting the platform. Metaflow provides some how-tos and tutorials, slack channels, and GitHub to drive the continuous improvement and adoption of Metaflow. As an open-source project, Metaflow benefits from contributions and feedback from a diverse and passionate community of users. Metaflow is distributed under the Apache License, Version 2.0, granting users the freedom to utilize, modify, and share the software without any cost.

Kedro is an open-source MLOps tool developed by QuantumBlack, a McKinsey company,  now part of the Anaconda ecosystem. It aims to streamline creating, managing, and deploying data pipelines for Machine Learning and data science projects. Kedro addresses the common challenges data scientists and engineers face when building complex data pipelines. These challenges include managing dependencies, version control, data versioning, and cataloging and deployment. The framework is built on top of familiar technologies such as Pandas, Dask, and PySpark. It provides a standardized project structure and conventions that make building and maintaining complex data pipelines easy.

Some of the offerings of Kedro include:

• Standardized Project Structure: Kedro promotes a standard project structure, enabling users to create organized, modular, and easily maintainable codebases.
• Data Catalog: Kedro offers a built-in data catalog that manages and abstracts access to various data sources and storage systems, simplifying working with diverse data formats.
• Pipeline Abstraction: Kedro allows users to define and manage their data pipelines using a simple, functional API. This abstraction makes reasoning about complex data workflows easier and improves code modularity.
• Data Versioning: Kedro automatically versions data artifacts, ensuring reproducibility and traceability throughout the ML lifecycle.
• Testing and Linting: Kedro promotes test-driven development and enforces code quality standards, ensuring the code is reliable, maintainable, and efficient.
• Documentation Generation: Kedro generates project documentation automatically, making it easier for team members to understand and collaborate on projects.

Kedro pipeline visualization

Kedro is a versatile framework that can be easily customized and integrates seamlessly with MLOps tools like MLflow and Apache Airflow, facilitating its incorporation into diverse data science workflows and environments. Kedro has a growing and active community of users and contributors who collaborate through platforms like GitHub, blogs, Slack, and documentation and guides. As an open-source project, Kedro benefits from diverse community contributions, which promote its growth and popularity. Continually refined and enhanced, Kedro’s Apache License 2.0 allows users to use, modify, and distribute the software without additional financial burdens. Its focus on modularity, testing, documentation, and comprehensive tools for managing data pipeline lifecycles make Kedro a favored choice among data scientists and engineers seeking scalable, reliable data pipelines.

Seldon Core is an open-source MLOps tool that focuses on deploying, scaling, and monitoring Machine Learning models in Kubernetes environments. It provides a standardized, flexible, scalable, and efficient way to serve models in production environments, enabling data scientists and ML engineers to streamline the model deployment process and efficiently manage their models in production. It also provides a flexible and modular architecture that can be customized to suit the needs of different use cases.

Seldon Core offers:

• Kubernetes-Native Deployment: Harnessing Kubernetes’ scalability, resilience, and flexibility for model deployment.
• Model Wrapping: Supporting various ML frameworks like TensorFlow, PyTorch, and scikit-learn with a standardized API.
• Advanced Inference Graphs: Enabling complex inference graphs for sophisticated ML pipelines with chained models, transformers, routers, and more.
• Monitoring and Observability: Providing built-in features like Prometheus metrics, request logging, and distributed tracing for tracking performance and troubleshooting in real time.
• Custom Deployment Strategies: Supporting rolling updates, blue-green deployments, and canary releases for safe and efficient model updates.
• Multi-Cloud and On-Premises Support: Compatible with cloud providers like AWS, Google Cloud, Azure, and on-premises Kubernetes deployments for diverse infrastructure requirements.

Seldon core high level, Source Seldon core

Seldon Core also has an enterprise product that provides enterprise-level support for its open-source MLOps software, allowing businesses to deploy ML models and experiments at scale with additional reassurance and security. The platform is platform-agnostic, allowing it to fit into any IT infrastructure across clouds and on-prem. It has a full SLA for support and resources, including direct access to the developers behind Seldon Core and a dedicated Customer Success Manager.

Seldon Core offers a wide range of resources, including blogs, webinars, research papers, newsletters, and documentation, to provide knowledge and thought leadership to its users. The platform has a vibrant and expanding community of users and contributors collaborating on platforms like GitHub and Slack. The continuous feedback and input from the diverse community foster the growth and adoption of Seldon Core.

Pachyderm is an efficient and scalable MLOps solution that empowers data engineering teams to automate intricate pipelines involving complex data transformations. Designed with scalability in mind, Pachyderm offers a cost-effective approach to managing extensive pipelines, making it an ideal choice for organizations looking to streamline their data engineering processes. It aids reproducible data processing, built to make building and maintaining end-to-end data pipelines easier

Some Key Features of Pachyderm:

1. Auto-triggering Pipelines: Data-driven pipelines are designed to initiate based on detected changes in data automatically.
2. Version Control: Pachyderm ensures immutable data lineage by providing data versioning for all data types.
3. Autoscaling and Parallel Processing: Built on Kubernetes, Pachyderm offers resource orchestration with autoscaling and parallel processing capabilities.
4. Automatic Deduplication: Pachyderm utilizes standard object stores for data storage and efficiently manages storage space with automatic deduplication.
5. Cloud and On-premises Compatibility: Pachyderm operates seamlessly across major cloud providers and on-premises installations, offering flexibility in deployment and management.

Pachyderm high level, Source Pachyderm

Pachyderm comes in two editions: Enterprise and Community. Enterprise Edition is licensed commercially and provides advanced features and unlimited potential, including unlimited data-driven pipelines, parallel processing, Role-Based Access Controls (RBAC), Pluggable Authentication, and Enterprise Support. On the other hand, Community Edition is available on GitHub and is ideal for those who want to explore Pachyderm’s capabilities or for small-scale projects.

Pachyderm offers learning resources, including blogs, customer use cases, eBooks, events and webinars, and a curated collection of technical examples on GitHub. Boasting a thriving and engaged community of users and contributors, This open-source project’s ongoing development and widespread adoption are fueled by its diverse community’s invaluable contributions and insights.

Ray is an open-source unified compute framework allowing users to scale their AI and Python workloads quickly without needing complex infrastructures. It is a flexible Python-native distributed computing framework that allows existing AI and Python applications to be easily parallelized on a laptop and scaled to a cluster on the cloud or on-premises with no code changes. Ray includes the Ray AI Runtime (AIR), which is a native set of best-in-class scalable ML libraries that enable scaling of the most compute-intensive ML workloads such as ML data pre-processing tasks via Ray Data, training large models via Ray Train, hyperparameter tuning via Ray Tune, reinforcement learning via Ray RLlib, batch inference via Ray Batch Predictor, and real-time inference via Ray Serve. Ray’s seamless integration with the Python and ML ecosystem allows non-experts to utilize distributed computing with simple Python APIs and familiar tools. Handling distributed execution aspects like task scheduling, auto-scaling, and fault tolerance, Ray lets engineers and researchers concentrate on application logic rather than distributed system operations.  Its ability to run code on both laptops and clusters improves developer productivity and iteration speed.

Unique Capabilities:

• Scalable: Ray offers parallel and distributed execution primitives, enabling developers to scale AI and Python applications with minimal code changes using Pythonic APIs.
• Unified: Ray AIR provides a comprehensive toolkit for ML workloads, including distributed training, hyperparameter tuning, inference, and real-time serving, with integrations for popular ML libraries and support for heterogeneous hardware.
• Portability: Ray supports easy portability, with Kubernetes support for on-premise deployments and native VM-level deployments on major public clouds, such as AWS, GCP, and Azure.

Ray high level, Source Ray

Ray offers various resources for support, learning, and community engagement, such as discussion forums, Slack channels, training, events, blogs, documentation, and newsletters. Users can follow the project, track issues, and contribute on GitHub, and success stories and integrated library ecosystems are available for reference. The Ray community enables members to connect, share best practices, contribute, and stay updated on the latest developments.

In conclusion, we’ve explored the top 10 open-source MLOps tools that can help you optimize and manage Machine Learning projects, ensuring that you stay ahead in the rapidly evolving AI landscape. Each tool offers unique features and capabilities, catering to various needs and requirements. Each tool is backed by a vibrant community and ecosystem, providing valuable support and resources to help you make the most of its offerings. By understanding the unique features and strengths of these MLOps tools, you can choose the best solutions to meet your specific needs and revolutionize the way you manage and optimize your Machine Learning projects.