MLOps for Governance and Compliance

Data, AI & Machine Learning Engineering Solution

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The adoption of Machine Learning carries a responsibility to ensure compliance with IT best practices, regulatory and ethical standards. To achieve this, organizations must establish processes that regulate access to ML models, enforce legal and regulatory guidelines, and monitor interactions with models and their outputs.

Additionally, organizations must maintain comprehensive documentation about each model, such as stakeholder involvement, business context, training data, feature selection, model reproducibility, parameter choices, and evaluation results. These practices collectively form the foundation of Model Governance, providing transparency, accountability, and compliance throughout the ML lifecycle.

Companies have always been required to comply with legal regulations, and the rise of ML/AI systems has introduced new obligations. To meet these regulations, organizations must implement strong model governance, ensuring transparency, risk management, and thorough documentation.

PAIN POINTs

Inconsistencies in training environments, missing metadata, and incomplete documentation can lead to unreliable results, lack of reproducibility, and non-compliance with regulatory requirements.
Lack of logging, versioning, and real-time alerting prevents organizations from detecting distribution shifts, model degradation, or security incidents in a timely manner, potentially leading to inaccurate predictions.
Vulnerabilities such as weak authentication and exposure to adversarial attacks can lead to unauthorized access, data breaches, and manipulation of AI systems.
Navigating regulatory compliance and auditability is increasingly complex, requiring automated governance frameworks, transparent reporting, and ongoing conformity testing to ensure compliance.
Fragmented workflows, the absence of a centralized model registry, and manual governance processes slow down AI deployments and increase operational costs.

solution

MLOps establishes best practices and a structured framework for managing Machine Learning lifecycle by standardizing processes for model development, deployment, and maintenance.

MLOps is increasingly recognized as crucial for effective governance: however, the optimal approach to blending ML model governance with MLOps is not a one-size-fits-all solution. The complexity of this integration varies significantly based on factors such as the number of models in production and the regulations in our business domain.

Bitrock fully integrates MLOps tools and practices into its services to establish a structured framework and best practices for managing the Machine Learning lifecycle.

By standardizing processes for model development, deployment, and maintenance, Bitrock recognizes the increasing importance of MLOps for effective governance and adopts a tailored strategy for its clients, based on the complexity and the specific regulations of the business domain.

More specifically, Bitrock’s adoption of MLOps results in the integration of automation into key stages of the ML lifecycle, including model iteration through CI/CD pipelines, allowing updates and improvements to be efficiently tested and deployed.

We implement advanced mechanisms for tracking and logging model artifacts, ensuring reproducibility and auditability. Additionally, our approach supports collaboration between data scientists, engineers, and compliance teams, ensuring alignment with organizational policies and regulatory requirements.

benefits

Optimal model performance through rapid bug fixes and performance monitoring.
Operational stability with automated pipelines and robust control to minimize downtime.
Traceability with standardized documentation and versioning.
Identification and mitigation of potential risks through continuous monitoring and alerting.
Regulatory compliance by automating reporting, auditability, and conformity testing.
Centralized tools and workflows for seamless cooperation between teams.

Technology Stack and Key Skills

Model Management & Tracking: MLflow, DVC, Weights & Biases
CI/CD & Automation: Kubeflow, Jenkins, Apache Airflow
Monitoring & Observability: Prometheus, Grafana, Evidently AI, Arize AI
Data Quality: Deequ, Great Expectations, Soda
MLOps Engineering: CI/CD, infrastructure automation, and deployment strategies.

Data & Model Governance: model drift detection, logging, real-time alerting, data lineage, model versioning, and compliance requirements.
Security & Risk Management: adversarial ML threats, access control mechanisms, and secure ML deployment practices.

MLOps for Governance and Compliance

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PAIN POINTs

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benefits

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MLOps for Governance and Compliance

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PAIN POINTs

solution

benefits

Do you want to know more about our services? Fill in the form and schedule a meeting with our team!

Milano

Treviso

Trieste

Chiasso

context