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How Platform Engineering Can Help Enterprises Comply with EU AI Act
Effective platform engineering integrates end-to-end observability and data governance, enabling scalable and compliant AI practices, writes Vultr CMO Kevin Cochrane.
Industry Perspectives
June 12, 2024
5 Min Read
Alamy
Platform engineering first gained critical mass by proving invaluable to application developers. It abstracted infrastructure complexity, automated DevOps processes, provided self-serve access to standardized tooling, and enforced security and compliance, all designed to make developers' jobs more accessible and efficient.
The next iteration of platform engineering — solutions specifically engineered for AI operations at scale — addressed the unique needs of data scientists and machine learning engineers building machine learning and large language models and deploying AI applications across distributed enterprises. These solutions featured AI infrastructure optimization, centralized development and training, and Kubernetes-based deployments to edge environments.
With the advent of the EU AI Act, a new frontier has opened for platform engineering — facilitating and automating the provisioning of model observability and data governance tools to ensure enterprises can institute responsible AI at scale.
The Dawning of the Age of AI Regulation
The EU AI Act is the first significant legislation by a world power to protect the public from irresponsible AI applications. For global enterprises that still need to operationalize responsible AI at scale, the act complicates AI operations significantly, and more countries are set to follow with similar legislation. To navigate this growing regulatory landscape, enterprises must embed responsible AI practices — focusing on model observability and data governance — at the core of their AI operations.
Ensuring compliance throughout the AI lifecycle is crucial. The only way to scale responsible AI practices across a distributed enterprise is through a platform engineering solution purpose-built for this task.
Enabling Responsible AI with End-to-End Observability
Operationalizing responsible AI at scale requires comprehensive observability across the AI/ML lifecycle. From initial training to deployment to continuous monitoring, observability ensures models operate ethically, securely, and in compliance with given standards or regulations.
Model Development: During training and fine-tuning, observability tools monitor data quality, algorithm performance, and hyperparameter impacts, establishing a reliable foundation and identifying potential biases before deployment.
Model Deployment: As models move to production, observability tracks real-world performance, detecting early signs of drift or degradation and validating adherence to ethical, data privacy, and security requirements.
Continuous Model Monitoring: Ongoing monitoring ensures models maintain accuracy and effectiveness over time, adapting to new data or conditions without compromising performance.
Collaboration and Feedback Loops: Observability facilitates structured feedback loops among data scientists, engineers, and stakeholders, aligning models with evolving business objectives, regulations, and ethical considerations.
By integrating end-to-end observability, enterprises can scale responsible AI practices, fostering trust in AI solutions while mitigating risks and ensuring regulatory compliance.
Data Governance Practices to Protect Privacy and Security
Effective data governance is crucial for maintaining responsible AI in edge environments, focusing on federated governance, data quality, AI model oversight, and robust security measures.
Federated Data Governance: A federated approach involves defining clear roles, such as data stewards and governance administrators, across business units, enabling business-friendly data terminology and ownership of data products/domains, and establishing governance councils to align practices organization-wide.
Data Quality and Lineage: High-quality data is vital for reliable AI model performance. Enterprises must implement data quality rules across hybrid environments, ensure traceability of data lineage and provenance, and automate data quality checks using AI/ML capabilities.
AI/ML Model Governance: Robust governance includes bias testing, model monitoring, enforcement of ethical AI principles like fairness, transparency, and privacy, and leveraging AI/ML to automate model validation, drift detection, and compliance checks.
Data Security and Privacy: Security is critical when processing sensitive data at the edge, where different jurisdictions uphold differing requirements. Enterprises must enforce data access controls and encryption, implement privacy-enhancing techniques like differential privacy and federated learning, and ensure compliance with local, federal, or regional regulations like GDPR and CCPA.
These governance measures allow organizations to maximize AI benefits while safeguarding against vulnerabilities in edge environments.
Platform Engineering and Responsible AI
Platform engineering accelerates an enterprise's AI journey by providing AI/ML engineers with the necessary tools and infrastructure and integrating robust governance and observability across AI/ML operations. Platform engineering teams must take specific steps to embed responsible AI components effectively:
Enable Self-Service Access with Integrated Observability: Automate access to AI/ML infrastructure, such as GPUs, CPUs, and vector databases, with built-in observability tools for real-time monitoring of model performance, data quality, and operational metrics.
Bridge Development and Operations with Observability Tools: Provide rapid experimentation capabilities governed by enterprise-grade security and reliability standards. Centralized observability frameworks within these tools ensure compliance and cross-team collaboration.
Offer Curated Templates with Built-in Governance and Observability: Provide vetted templates for common AI/ML workflows, pre-equipped with observability features to ensure adherence to data privacy, ethics, and compliance standards.
Automate Workflows with Observability Checks: Use AI and automation to streamline the AI development lifecycle from testing to deployment to monitoring, integrating checks for model drift, bias detection, and ethical AI usage.
Establish a Responsible AI "Red Team": Provide self-serve access for a dedicated team to test, tune, eliminate errors, weed out biases, and validate models before moving them to the enterprise's central model registry.
Scale AI Adoption Cost-Effectively with Observability Infrastructure: Guide AI/ML infrastructure adoption with a balanced approach to scalability and cost-effectiveness, supporting extensive data monitoring and management capabilities.
Embedding these strategies within platform engineering solutions ensures AI operations are efficient, innovative, and aligned with rigorous standards for responsible AI. This approach guarantees AI systems are transparent, compliant, and ready to meet the challenges of a rapidly evolving technological landscape.
Looking Ahead
As more governments introduce AI regulations patterned after the EU AI Act, enterprises that do not adopt platform engineering solutions specifically designed to incorporate responsible AI tools will be mired in the resource-consuming work of continuously adapting as new legislation is enacted across a dynamic regulatory landscape.
Conversely, platform engineering solutions built for responsible AI at scale — facilitating model observability and data governance — will future-proof the enterprise's distributed AI initiatives. These solutions enable enterprises to scale AI operations in edge environments globally, ensuring compliance with varying regulations while meeting customer demands. By embedding robust observability and governance, enterprises can confidently navigate regulatory complexities, maintaining efficiency, innovation, and ethical standards across all AI operations.
About the author:
Kevin Cochrane is Chief Marketing Officer at Vultr.
About the Author
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