DevOps and AI Series: Landing Zones
In today’s fast-paced digital landscape, the integration of artificial intelligence (AI) and DevOps practices is revolutionising how organisations manage their infrastructure and streamline operations. This blog post, the first in a series, will delve into the critical importance of establishing safe and scalable landing zones for AI deployments and how DevOps teams enable this transformation.
AI and DevOps may seem like distinct entities, but their convergence can significantly enhance the efficiency and scalability of IT operations. AI technologies, powered by platforms like Azure OpenAI or Azure AI Foundry, offer unprecedented capabilities for automating processes, analysing vast amounts of data, and making intelligent decisions. However, to fully leverage these benefits, it’s crucial to build safe and scalable landing zones that support seamless AI integration.
What are Landing Zones?
Landing zones are foundational environments that provide a secure and compliant foundation for deploying workloads in the cloud. They establish the core infrastructure components, governance policies, and security controls necessary to support the organisation’s cloud strategy. In the context of AI deployments, landing zones play a critical role in ensuring that AI models and applications can be deployed, managed, and scaled effectively.
AI Landing Zone Components
When designing a landing zone for AI deployments, several key components must be considered along with the separation of ownership and responsibilities between DevOps and other teams such as application, platform and data science teams.
The following components are typically owned and managed by the application and data science teams:
Azure OpenAI or Azure AI Foundry: These platforms provide the tools and services needed to develop, train, and deploy AI models. Application and data science teams are responsible for managing the AI workloads and ensuring that they meet the required performance and scalability standards.
Azure App Service: This platform enables the deployment of web applications. Application teams are responsible for managing the deployment and scaling of these services to support AI applications.
AI Search: Azure AI Search provides AI-powered search capabilities for applications. Data science teams are responsible for configuring and managing the search indexes and query pipelines to deliver relevant search results.
Azure Monitor and Application Insights: These tools provide monitoring and telemetry capabilities for AI applications. Data science and application teams are responsible for setting up monitoring alerts, tracking performance metrics, and diagnosing issues.
Azure Application Gateway: This service provides secure ingress access to AI applications. This component can either be managed by the application team or as a centralised resource managed by the platform team, depending on the organisation’s security and compliance requirements.
The following components are typically owned and managed by the Platform and DevOps team:
Azure Firewall: This service provides network security and threat protection for AI workloads. The platform and DevOps teams are responsible for configuring and managing the firewall rules to ensure that AI applications are protected from external threats.
Azure Policy: This service enables the enforcement of governance policies and compliance standards across the Azure environment. The platform and DevOps teams are responsible for defining and enforcing policies that govern the use of AI resources and services.
User-Defined Routes: These routes define the path that network traffic takes within the Azure environment. The platform and DevOps teams are responsible for configuring and managing these routes to ensure that AI workloads can communicate with other services and force traffic to the hub network.
Azure Bastion: This service provides secure remote access to AI workloads. The platform and DevOps teams are responsible for configuring and managing the bastion host to ensure that access to AI resources is restricted to authorised users.
DevOps in AI Deployments
DevOps practices play a pivotal role in supporting AI deployments by fostering a culture of continuous integration, continuous delivery (CI/CD), and automation. Key DevOps responsibilities include:
Infrastructure Provisioning: DevOps teams use infrastructure as code (IaC) tools to automate the provisioning of scalable, secure, and reliable infrastructure on platforms like Azure. This ensures that AI models have the necessary computational resources and storage capacity.
Security and Compliance: Establishing robust security protocols and compliance measures to protect sensitive data and meet regulatory requirements. DevOps teams implement components such as IAM policies and network configurations to secure the AI infrastructure.
Collaboration Between Teams
An area that is often overlooked is the role of DevOps in deploying and managing AI models. DevOps teams need to work closely with application and data science teams to assist with:
Continuous Integration and Continuous Delivery (CI/CD): Integrating AI model updates seamlessly into the existing infrastructure. DevOps teams implement CI/CD pipelines to automate the deployment of AI models, ensuring rapid and reliable releases.
Automated Testing and Validation: Ensuring AI models are tested and validated through automated testing frameworks, reducing the risk of errors and ensuring consistent performance.
Monitoring and Maintenance: Using monitoring tools to track the performance of AI models and make necessary adjustments. DevOps teams set up comprehensive monitoring and logging mechanisms to ensure AI services are running optimally and can quickly address any issues that arise.
Conclusion
I recently had the opportunity to speak at the Melbourne Azure User Group about AI Landing Zones. I emphasised the importance of collaboration among all teams to understand the AI workloads and ensure that the landing zones are designed to meet both the performance and security requirements of the organisation.
For a deeper dive into the topic, you can find my presentation and a demo on deploying a secure AI landing zone using a DevOps approach on my GitHub repository.
In the next post, we’ll dive deeper into the role of DevOps in managing AI models and explore best practices for integrating AI into your DevOps processes. Stay tuned for more insights on how AI and DevOps are transforming the digital landscape!