The EU AI Act has introduced strict guidelines to regulate the development and deployment of AI systems across organizations operating in the EU (European Union) and those based outside but with operations and users in the EU. As a data scientist or machine learning engineer, it’s crucial to understand how these regulations affect your work. Non-compliance could lead to heavy penalties, including a fine of over 35M euros.

To help you navigate these new requirements and avoid potential risks, we've curated a list of machine learning operations (MLOps) tools that align with the EU AI Act. By integrating these tools into your workflows, you can ensure your AI solutions are not only innovative but also responsible and compliant with EU regulations.

TL;DR List of MLOps tools that comply with the EU AI Act
To give you a quick glance at the tools, here’s a summary of 10 MLOps tools that adhere to the regulations:

Opensource tools:

1. KitOp: A versioning system ensuring transparency, centralized and tamperproof record keeping of all AI project assets.
2. Kubeflow: Offers end-to-end pipeline orchestration with robust observability and human oversight.
3. MLflow: Provides strong governance, tracking data, and experiment management.
4. ZenML: Focuses on data integrity and workflow standardization.
5. ClearML: Manages the ML lifecycle with strong data preparation and versioning.
6. H2O.ai: Emphasizes responsible AI, focusing on data integrity and bias detection.

Proprietary tools:

1. DataRobot: Supports automated ML with a focus on governance and monitoring.
2. Comet: Tracks experiments and monitors model performance for compliance.

Tools offering a blend of both:

1. Weights and Biases: Tracks experiments, ensuring transparency and reproducibility. It’s largely proprietary but offers open source components.
2. Fiddler AI: Enhances explainability and model monitoring, blending proprietary tools with open source features.

Let's explore the EU AI Act in detail and consider how these tools align with the regulation.

What is the EU AI Act?

The EU AI Act is a legal framework designed to govern the deployment and use of AI systems in the European Union. According to the document, AI applications are categorized into four levels of risk:

• Unacceptable risk: AI systems that are considered harmful are fully banned by the EU AI Act. This includes AI systems that exploit mental health or other vulnerabilities in specific groups, such as children.

• High risk: AI systems that pose a significant danger to basic human rights, such as healthcare, education, and transport.

• Limited risk: AI systems that pose moderate risks and require transparency measures like informing users that they are interacting with an AI system.

• Minimal risk: AI systems that do not pose risks to the rights or safety of individuals.

This classification influences how much regulator involvement will be required and the level of compliance needed for any given AI system. Additionally, AI applications that present an unacceptable risk are prohibited. At the same time, high-risk systems (like those in healthcare or law enforcement) must comply with strict transparency, accountability, and data governance requirements. For more details, refer to the EU AI Act blog.

How does the EU AI Act impact MLOps tools?

The EU AI Act significantly impacts MLOps tools by introducing new compliance requirements that data and engineering teams must comply with. These requirements focus on transparency, data governance, human oversight, and risk management, particularly for AI systems designated as high-risk applications.

Key compliance requirements for AI systems under the EU AI Act.

The Act outlines a clear set of compliance requirements for AI systems designed to foster trust, ensuring that AI systems are accountable, safe, and transparent, particularly for high-risk applications.

Here’s how the new compliance requirements have impacted MLOps tools:

• Transparency: MLOps tools should provide clear insights into how models are trained, deployed, and monitored. These tools should be able to log hyperparameters, the dataset used, and all other metadata required to ensure transparency for compliance with the EU AI Act.
• Human oversight: MLOps tools must facilitate capabilities that enable human oversight in cases. This ensures that humans can oversee high-risk AI applications to prevent harmful outcomes.
• Data governance: MLOps tools should incorporate strong data governance measures to ensure that the use of training models is accurate and unbiased.
• Record Keeping: MLOps tools should have long-term records of activities carried out by the AI system.

10 MLOps tools that comply with the EU AI Act.

The nature of high-risk AI systems in the EU makes it imperative to adhere to strong regulations for MLOps tools to comply with the EU AI Act. Based on certain selection criteria such as transparency, human oversight, data governance, and compliance, below is a list of tools that comply with the EU AI Act.

1. KitOps

KitOps is an open source, standards-based packaging, and versioning system designed to enhance collaboration among data scientists, application developers, and Site Reliability Engineers (SREs) working on integrating or managing self-hosted AI/ML (Artificial Intelligence/Machine Learning) models.

Article 12 of the EU AI Act specifies that MLOps tools should have long-term records of activities of about 10 years carried out by the AI system. This includes logs of processing data, training models, and system changes over time. In that line, KitOps’ trait as an OCI (Open Container Initiative) artifact and reliable tagging system establishes lineage across your ModelKit versions. It creates visibility into the origin and evolution of your ModelKit artifacts (i.e., models and model assets). KitOps’ ModelKits can be created from Jupyter Notebooks or as part of a pipeline. ModelKits are stored in an enterprise’s container registry, which is already secured.

The system uses immutable, content-addressable storage, which doesn't allow two ModelKits to have the same content version. This tagging system eliminates the possibility of ML engineers having ModelKits with the same model and model assets, ensuring that each model and model asset is unique.

Furthermore, KitOps enables your data scientists to version their models and model assets when they package their ModelKit. This feature ensures that each ModelKit’s models and assets remain consistent. Therefore, your developers can confidently retrieve and deploy specific models created by the data scientists without the risk of confusion.

1. Kubeflow

Kubeflow is an open source platform designed for the seamless deployment, scaling, and monitoring of machine learning workflows. It offers end-to-end pipelines that ensure observability throughout the entire machine-learning process. This includes facilitating human oversight and enabling data science teams to perform quality checks at each stage of training and deployment.

With its robust pipeline orchestration features, Kubeflow empowers data science teams to manage the entire lifecycle of machine learning workflows—from data processing to model training, deployment, and monitoring. This approach promotes transparency at every stage of the process.

Moreover, Kubeflow supports ongoing monitoring and risk management by providing continuous model monitoring in production. This capability helps identify model drift, bias, and performance issues, aligning with the need for regular oversight of AI systems.

1. MLflow

MLflow is an open source platform for managing end-to-end machine learning lifecycle —including experimentation, reproducibility, and deployment. It supports strong governance by tracking data and validating the models. It allows the machine learning teams to log and manage experiments, including model metrics, parameters, and artifacts. This facilitates the reproducibility of results, which is crucial for transparency in AI systems.

Beyond that, the model registry serves as a centralized store for managing models throughout their lifecycle, including model versioning and stage transitions; thus, organizations can keep detailed records of model performance and versions that comply with the Act.

1. ZenML

ZenML is an MLOps framework that simplifies and standardizes the end-to-end machine learning (ML) workflows. Especially when it comes to compliance with regulations like the EU, its features and user selling points (USPs) make it stand out in MLOps. By focusing on data integrity, model quality, and monitoring throughout the model development process, ZenML supports mitigating risks associated with deploying AI systems.

Additionally, ZenML streamlines the machine learning workflow to support best practices such as data preparation, model training, and model testing. Its architecture enables data scientists to define each step of the workflow clearly. This helps to account for the fact that every step of the model development is checked and complies with the Act's requirement for proper documentation.

1. Comet

Comet is a full-stack experiment tracking platform that automatically tracks everything from input data to model development, machine learning model deployment, and model management. It enables data science teams to monitor model metrics and understand how their experiments have evolved over time.

Furthermore, Comet logs all experiments in detail as part of compliance to ensure traceability and documentation requirements for regulations like the EU.

As a supplement, it enables continuous model performance monitoring. With this, data science and machine learning teams can detect changes in model behavior over time that may indicate degradations. This helps to mitigate risks by identifying issues such as model drift or data drift, which can lead to biased outcomes.

1. DataRobot

DataRobot offers automated machine learning (AutoML) functionality with good model explainability, monitoring, and governance possibilities. These features help keep AI models fair, accountable, and transparent in compliance with the regulations.

Beyond that, DataRobot enables data scientists to build, deploy, and monitor models efficiently. This continuous monitoring is crucial for detecting when an AI model degrades in performance, which is vital for compliance with the act’s regulations for risk management.

1. ClearML

ClearML is a full-stack MLOps platform for managing machine learning workflow. It is robust for data preparation and versioning and ensures that the data used in model training is reliable. ClearML supports experiment tracking, which allows data scientists to document their machine learning experiments in line with the regulations.

Moreover, ClearML supports monitoring model performance and conducting thorough model testing before deployment, ensuring the model meets necessary quality standards.

1. Fiddler AI

Fiddler AI is an explainable AI platform that enhances the machine learning workflow. It makes it easier to create transparency and trust in AI systems. The platform enables data scientists to understand and interpret model predictions efficiently.

Fiddler AI also supports continuous monitoring of machine learning models to track models' performance and detect data drift. This helps mitigate the risks of models degrading over time.

1. H2O.ai

H2O.ai develops enterprise AI tools for machine learning and deep learning, including features for automated model building, explainability, and bias detection. It concentrates on responsible AI, which helps companies to comply with legal and ethical standards.

H2O.ai emphasizes data integrity and versioning. It ensures that critical steps such as data preparation and versioning are checked to ensure the data quality used in model training. This complies with regulations and requires reliable data input.

Beyond that, H2O.ai also supports model governance by providing tools for managing model versions and metadata.

1. Weights and Biases

Weights and Biases focus on experiment tracking, data logging, and model monitoring. It allows tracking of your model training process, hyperparameters, and which datasets you use, making it easier to ensure transparency and reproducibility.

W & B provides a robust system for tracking machine learning experiments, from data logging to model development, optimizing machine learning models, monitoring model metrics, and model quality. This ensures that every part of the machine learning workflow is fully documented, traceable, and accountable.

Final considerations

As AI regulations evolve under the EU AI Act, MLOps tools must continuously adapt to new compliance requirements. Tools must support features like transparent documentation, risk assessment, and human oversight. MLOps platforms should integrate mechanisms that ensure ongoing adherence to these regulations, especially for high-risk AI systems.

Developers and engineering leaders should focus on adopting tools that are designed to keep up with regulatory changes. It’s important to choose platforms that simplify compliance management and offer long-term support for adhering to evolving regulations.

By providing a framework that simplifies the setup and enforces compliance, KitOps helps teams stay focused on innovation rather than regulatory complexity. If you're ready to streamline your MLOps processes while adhering to the latest standards, consider signing up on KitOps to get started.