7 Best MLOps Tools [2025 Buyer’s Guide]
Updated Jan 2, 2025
Introduction
Organizations that struggle with machine learning workflows, model deployment issues, lack of visibility into model performance, and other challenges are increasingly turning to new machine learning engineering practices. Machine learning operations (MLOps) is an important function that aims to use automation and standardization to get high-quality models into production faster, as well as facilitate reproducibility and iteration.
A typical MLOps pipeline involves ingesting data and using feature engineering to uncover relevant input variables for machine learning models to use. Afterward, models can be trained and deployed into production. Once a model is operational, continuous monitoring and retraining are used to maintain and improve performance over time.
In this guide, we’ll explore how MLOps tools can simplify and automate nearly every aspect of an MLOps pipeline. We’ll also cover the different categories of MLOps tools, highlight the top solutions in each category, and provide a framework for evaluating and choosing the right tools for your organization. By streamlining these processes, MLOps tools empower teams to build and deploy AI solutions faster and more reliably,
Understanding MLOps Tools
MLOps tools can be categorized based on their main functions within the MLOps pipeline. By understanding these categories, organizations can choose the tools that align with their unique needs.
- MLOps Platform: Provides a comprehensive solution that includes capabilities for every aspect of the MLOps pipeline, from data ingestion through to model deployment and monitoring.
- Experiment Tracking and Model Management: Manages machine learning experiments and different model versions to ensure they can be shared and reproduced.
- Data Versioning and Management: Tracks changes to data, maintains data lineage, and ensures data quality for machine learning projects.
- Feature Stores: A centralized repository for storing, managing, and serving features (measurable characteristics within a dataset) to machine learning models.
- Model Serving and Deployment: Streamlines the process for delivering machine learning models to production environments.
- Model Monitoring and Observability: Tracks metrics and provides insights into model performance in production.
- Workflow Orchestration: Automates and coordinates various steps in the MLOps pipeline.
It’s worth noting that some tools may span multiple categories and offer more comprehensive solutions. This means the best tool for your organization depends on a project’s unique requirements, your team’s skills, and your existing technology infrastructure.
7 Best MLOps Tools by Function
Let’s explore the top MLOps tools in each category, focusing on their key features, integrations, scalability, and other criteria.
MLOps Platform
Best Tool: Anaconda
Anaconda is a comprehensive MLOps, data science, and AI platform.
- Features and Capabilities: Includes features and integrations that cover most aspects of data workflows.
- Open-Source vs Proprietary: Open-source core with enterprise options available.
- Security Measures: Robust package verification and secure repository.
- Integrations: Integrates with various IDEs, cloud platforms, and data science tools.
- Cost and Pricing Models: Individual, academic, and enterprise plans available.
- Scalability and Performance: Highly scalable, suitable for individual users to large enterprises.
Experiment Tracking and Model Management
Best Tool: Neptune
Neptune is an experiment tracker for foundation model training.
- Features and Capabilities: Includes real-time experiment tracking capabilities for thousands of metrics.
- Open-Source vs Proprietary: Proprietary solution.
- Security Measures: SOC 2 compliant and employs multiple layers of security to protect data.
- Integrations: Integrates with various machine learning libraries, CI/CD tools, and more.
- Cost and Pricing Models: Free and paid plans available.
- Scalability and Performance: Highly scalable architecture and can be deployed on-premise or in a private cloud.
Data Versioning and Management
Best Tool: lakeFS
lakeFS is a data version control system for data lakes.
- Features and Capabilities: Manages data in a variety of formats as code.
- Open-Source vs Proprietary: Open-source and proprietary cloud options available.
- Security Measures: SOC 2 compliant and provides numerous security features.
- Integrations: Integrates with popular solutions for nearly all aspects of a data stack.
- Cost and Pricing Models: Free and paid plans available.
- Scalability and Performance: Maintains high performance over data lakes of any size
Feature Stores
Best Tool: Feast
Feast is an open-source, end-to-end feature store.
- Features and Capabilities: Includes capabilities to store and serve features.
- Open-Source vs Proprietary: Open-source.
- Security Measures: Supports standard encryption, authentication, and authorization methods.
- Integrations: Supports all major public clouds and other data sources.
- Cost and Pricing Models: Free and open-source solution.
- Scalability and Performance: Designed to work at scale and support low latency feature serving.
Model Serving and Deployment
Best Tool: Seldon Core
Seldon Core is a software tool for deploying models into production.
- Features and Capabilities: Includes model versioning, scaling, and automatic rollbacks to streamline model deployments.
- Open-Source vs Proprietary: Open-source for non-production use only.
- Security Measures: Advanced user management, audit trails, logging, and alerts to safeguard data.
- Integrations: Extensive integration library for third-party tools.
- Cost and Pricing Models: Annual flat license fee for production use.
- Scalability and Performance: Model and server auto-scaling to optimize performance.
Model Monitoring and Observability
Best Tool: Fiddler AI
Fiddler AI is an enterprise AI observability platform for ML models and LLM applications.
- Features and Capabilities: Includes features to monitor, analyze, and protect models in training and production.
- Open-Source vs Proprietary: Proprietary platform.
- Security Measures: SOC 2 and HIPAA compliance.
- Integrations: Integrates with data pipelines, ML platforms, and alerting tools.
- Cost and Pricing Models: Lite, Business, and Premium plans available.
- Scalability and Performance: Highly scalable for enterprise use cases.
Workflow Orchestration
Best Tool: Apache Airflow
Apache Airflow is an open-source workflow management platform for data engineering pipelines.
- Features and Capabilities: The ability to create workflows using pure Python or an intuitive UI.
- Open-Source vs Proprietary: Open-source.
- Security Measures: Offers security features like access control, API authentication, and more.
- Integrations: Integrates with many popular technologies to execute tasks on public cloud infrastructure and other third-party services.
- Cost and Pricing Models: Free and open-source.
- Scalability and Performance: Modular architecture for maximum scalability.
MLOps Tools vs Platforms
Individual MLOps tools typically perform specific functions within the MLOps pipeline. The limited scope of individual tools gives experienced data scientists the option to select multiple solutions and fully customize an MLOps pipeline to meet an organization’s unique requirements.
MLOps platforms like Anaconda are more comprehensive, combining different technologies to cover multiple functions within an MLOps pipeline. This often includes various machine learning capabilities as well as project management and collaboration tools. MLOps platforms are typically designed to support the entire end-to-end machine learning lifecycle. They are also more accessible and often have a shorter learning curve for less technical users.
How to Choose the Right MLOps Tool
When choosing an MLOps tool, it’s important that it aligns with the unique needs of your organization. Here are some factors to consider when evaluating potential solutions:
- Ease of Use: Assess whether the tool will be easy to adopt and use for your team and organization. Collaboration features like version control, task scheduling, and code sharing are also useful for streamlining machine learning workflows.
- Interoperability: Evaluate how the tool will fit into your existing infrastructure and data sources. Consider whether there are out-of-the-box integrations with popular tools and libraries.
- Scalability: Identify any potential barriers to scalability and performance. It’s important to choose a tool that can handle increasing volumes of data as your needs evolve.
- Community: Look for an active community and ecosystem that can provide support and contribute to the continuous improvement of the tool and its third-party integrations.
- Open-Source: Consider whether a tool built with open-source technologies can meet your requirements before locking yourself into a proprietary solution.
See Why Anaconda is a Powerful MLOps Platform
MLOps platforms offer several advantages over MLOps tools. They provide a comprehensive solution that covers the entire MLOps pipeline, from data ingestion to model deployment and monitoring.
Anaconda is the leading data science and MLOps platform, with 45 million users and a thriving community. The core platform is built atop secure open-source packages and the vibrant Python ecosystem, and comes with numerous pre-installed packages for data science and machine learning. This means you can quickly get started with Anaconda to transform your MLOps workflows.
In addition, Anaconda enterprise solutions offer features specifically designed for MLOps. This includes tools for tracking, versioning, and securely deploying models at scale. The platform also provides workspaces for teams to collaborate on projects, share environments, and manage workflows.
Request a demo to see if Anaconda is right for your organization’s MLOps and data science workflows. Or, if you’re curious to experiment with Anaconda on your own, you can get started for free.