Top 10 MLOps Tools for 2025
MLOps tools are crucial for simplifying the development and management of machine learning models. They automate tasks and ensure reproducibility, helping organizations maximize the value of their ML investments.
With the rapid growth of AI, MLOps tools are becoming a must-use for research and development teams. These tools simplify the development, deployment, and management of machine learning models, making complex processes more manageable.
There’s a huge demand for ML support. 86% of organizations needed help generating business value from their machine learning (ML) investments in 2023. Hence, MLOps tools address these issues by automating recurring tasks, ensuring reproducibility, and freeing up teams to focus on innovation.
What are MLOps Tools?
MLOps, which stands for Machine Learning Operations, is a set of practices that weave machine learning into software and data engineering. It involves using processes and tools to automate development and deployment and maintaining machine learning models at scale in production.
MLOps tools are specifically designed to support best practices related to machine learning. They focus on tasks such as version control of models, automating data pipelines, monitoring models, and conducting automated testing and validation.
These tools assist data scientists and software engineers in managing the entire lifecycle of machine learning models, including training and monitoring, ensuring models perform consistently and reliably in production.
Types of MLOps Tools
- Model Versioning Tools: Allow users to consistently manage and compare model versions to reproduce results. For example, tools like DVC and MLflow facilitate the versioning of machine learning models and datasets.
- Pipeline Orchestration Tools: Include data preprocessing, model training, evaluation, and deployment. For example, tools like Kubeflow and Apache Airflow automate different steps in the machine learning process.
- Monitoring and Management Tools: Monitor metrics such as accuracy, latency, and resource utilization and can detect anomalies and performance degradation.
- Deployment Tools: Support various deployment strategies, ensuring the safe and efficient rollout of new models. Tools like TensorFlow Serving and AWS SageMaker simplify the deployment of machine learning models to production.
Benefits of MLOps Tools
- Improved Collaboration: Enable better collaboration among data scientists, machine learning engineers, and operations teams, leading to more efficient and effective teamwork.
- Enhanced Automation: Automate tasks such as data preprocessing, model training, and deployment, freeing up time for more advanced work and ensuring more consistent and reliable processes.
- Increased Scalability: Simplify scaling machine learning operations and handling increased data volumes and deployment across various environments without compromising performance or reliability.
- Effective Model Management: Simplify the lifecycle management of machine learning models through versioning control, monitoring, and logging.
- Faster Time to Market: Automatically deploy models to production, enabling teams to gain a competitive advantage by quickly delivering solutions to the market.
Key Features to Look for in MLOps Tools
- Automation and Orchestration: MLOps tools should prioritize automating and orchestrating data preprocessing, model training, and deployment tasks.
- Scalability: MLOps tools should be capable of scaling with dataset size and computational requirements growth. This feature ensures the tools can handle increasing model size and complex operations without losing performance and reliability.
- Monitoring and Logging: Monitoring and logging are essential for running any model as they allow real-time performance tracking and problem identification.
- Seamless Integration: An essential aspect of MLOps tools is their ability to seamlessly integrate with existing tools and platforms to ensure smooth workflows. Supporting well-known data science and DevOps tools is crucial for easy integration and minimal disruptions to existing workflows.
Top 10 MLOps Tools for 2024
1. ModelBit
ModelBit is a machine learning engineering platform with built-in MLOps tools that simplify the deployment and management of machine learning models.
Main Features:
- Real-time monitoring and alerts.
- Automated versioning and rollback.
- Easy integration with popular data science tools.
Best For:
Startups and small teams looking for quick and reliable model deployment.
Price:
Pricing varies based on workloads and duration. Offers $25 free in credit.
Review: “Supports custom environments, packages, and tests. Can also run training jobs from a notebook or from Git. Includes logs, monitoring, and version control.”
2. Control Plane
While not directly an MLOps platform, Control Plane offers features and capabilities that can be highly relevant and beneficial in an MLOps context. For example, Kubernetes is a popular choice for orchestrating and scaling machine learning workloads. Control Plane’s expertise in Kubernetes workloads can help MLOps teams effectively deploy, manage, and scale their ML models and pipelines in a cloud-native environment.
Main Features:
- Uses Capacity AI technology to optimize cloud costs and resource usage by automatically scaling applications based on demand.
- Offers robust collaboration tools that enable seamless integration with CI/CD pipelines.
- Universal Cloud Identity™ technology allows workloads to run across any combination of cloud providers or on-premises infrastructure.
- Supports serverless mode with automatic scaling to zero when not in use, billed by millicores and megabytes of memory.
Best For:
Teams using Kubernetes for orchestrating and scaling ML workloads.
Price:
Clear and simple pricing based on your usage, so you never overprovision. If you have a Kubernetes cluster, try the free K8s cost calculator to see your cost savings when running workloads on the Control Plane platforms versus running them on your cloud provider.
Review: “Thanks to Control Plane, we’ve mastered multi-cloud management, fine-tuned Kubernetes efficiency, and saved substantially on costs.”
3. Pachyderm
Pachyderm is an MLOps solution with data versioning and end-to-end pipelines. It focuses on reproducibility and scalability for machine learning workflows.
Main Features:
- Data versioning and lineage tracking.
- Scalable data pipelines.
- Git-like operations for data science.
Best For:
Organizations needing robust data versioning and reproducibility.
Price:
Free tier available, and pricing is by inquiry.
Review: “Ability to keep branches of your data sets when you are testing new transformation pipelines.”
4. Dagster
Dagster is an orchestration platform for developing, deploying, and managing data pipelines, supporting reliable and maintainable machine learning workflows.
Main Features:
- Integrates with popular data tools like Airbyte, Snowflake, and Slack.
- Built-in data asset management.
- Flexible and extensible design.
Best For:
Teams needing to orchestrate and manage complex ML workflows and build data pipelines.
Price:
Offers three packages, Solo, Starter, and Pro, starting from $10.
Review: “Dagster is designed as a cloud-native orchestrator to simplify the development, production, and observation of data assets.”
5. Kubeflow Pipelines
Kubeflow Pipelines is a platform for deploying, orchestrating, and managing secure Kubernetes ML workflows.
Main Features:
- End-to-end orchestration of ML workflows.
- Reusable pipeline components.
- Offers tools for each stage of the ML lifecycle, including pipelines and model training.
Best For:
Kubernetes users looking for a comprehensive MLOps solution.
Price:
Open source (free).
Review: “The all-in-one feature of Kubeflow has made the team easy to use and has saved a large amount of time. This is easy to use for new learners.”
6. MLflow
MLflow is an open-source platform for managing the end-to-end ML lifecycle, including experimentation, reproducibility, and deployment. It includes tools for tracking and sharing models.
Main Features:
- Experiment tracking and management.
- Model registry and deployment.
- Integration with popular ML libraries.
Best For:
Teams needing experiment tracking and model lifecycle management.
Price:
Open source (free).
Review: “MLflow helps streamline the entire ML lifecycle with a simple setup and intuitive interface, enabling teams to reproduce results and collaborate easily.”
7. Comet ML
Comet ML is a customizable platform for tracking, comparing, and optimizing machine learning models. It integrates with popular frameworks like PyTorch, XGBoost, and others through its open API.
Main Features:
- Experiment management, tracking, and visualization.
- Team collaboration tools.
- Model production monitoring.
Best For:
Data science teams looking for advanced experiment tracking.
Price:
Free plan available. Paid plans start at $50/month.
Review: “I needed a tool that would help me in keeping track of my experiments. I got a whole set of tools that are perfect for my ML research.”
8. LakeFS
LakeFS is an open-source data version control tool that transforms your object storage into Git-like repositories.
Main Features:
- Git-like version control for data lakes.
- Seamless integration with existing data tools.
- Scalable and efficient data management.
Best For:
Teams working on MLOps projects that need to manage large data lakes and require version control.
Price:
Open source.
Review: “LakeFS helps to transform data into a usable and livable form. It is easy to see snapshots of the data instead of being overwhelmed with everything.”
9. DVC
DVC is a version control system for ML projects. It integrates with Git to manage datasets, track experiments, and reproduce results effectively, enabling teams to streamline tasks like experiment tracking.
Main Features:
- Data versioning and management.
- Experiment tracking and reproducibility.
- Integration with Git.
Best For:
Developers looking for a lightweight data versioning solution.
Price:
Open source (free).
Review: “DVC allowed me to have an overview of my results, with plots and tracking the metadata. This improves and speeds up the research process, allowing reproducibility of the results and better teamwork.”
10. Databricks
Databricks is a unified analytics platform combining data engineering, data science, and machine learning. It offers collaborative tools and scalable cloud infrastructure for MLOps teams.
Main Features:
- Unified data analytics and machine learning platform
- Collaborative notebooks
- Scalable and optimized for big data
Best For:
Organizations needing a unified platform for data and ML.
Price:
Offers a free trial and a pay-as-you-go pricing model.
Review: “The greatest upside to the Databricks Platform that’s constantly being developed. Databricks is developing [new] code and utilities to run on this platform.”
Maximize Your MLOps Potential with Control Plane
Choosing the right MLOps tools can significantly improve your machine learning workflows. One tool that stands out is Control Plane. With its robust features and seamless integration capabilities, Control Plane offers invaluable support for deploying, managing, and scaling your ML models in a cloud-native environment.
With Control Plane, you can run workloads across any combination of cloud providers and on-premises infrastructures using the Universal Cloud Identity™ technology. Mix services from AWS, GCP, and Azure effortlessly, then leverage Capacity AI to automatically scale costs, so you only pay for what you need. Enjoy the freedom to run workloads agnostically with 99.999% availability and ultra-low latency. Book a Demo to see how Control Plane can revolutionize your ML operation.