AIRE

AI Reliability Engineering (AIRE) - concept of a robust framework for AI and ML that applies the principles and practices of Site Reliability Engineering (SRE) to AI systems

Framework Overview

• AIRE aims to make AI products more reliable for business
• AIRE should providing a comprehensive framework, methods and toolkit to enable AI and ML practitioners, Data Engineers, DevOps and SRE to develop, delivery and operate AI products
• AIR Engineering should offers an approach to addressing challenges, combining cutting-edge tools and technologies with best practices from the fields of SRE, DevOps and MLOps.

AWS Reference FM/AIOps

FMOps/LLMOps: Operationalize generative AI and differences with MLOps

Reference OpenAI Case

Scaling Kubernetes to 7,500 nodes

Reference GTP-4 Architecture

GPT-4 Architecture, Infrastructure, Training Dataset, Costs, Vision, MoE

Reference Ray Framework

Effortlessly scale your most complex workloads

Components

• AIRE Framework: A set of guidelines, best practices, templates, checklists and examples for applying AIRE to different types of AI products
• AIRE Toolkit: A collection of open source tools and technologies that support the implementation of AIRE practices.

Problem Statement

AI products and solutions are becoming increasingly important for businesses to gain competitive advantage, improve customer experience, and optimize operations. Deploying and integrating AI products into an organisation's perimeter requires new approaches to reduce the risks of data leakage or loss and ensure security. It requires a lot of skills, tools, and processes to ensure that the AI products are reliable, secure, trustworthy, and aligned with business goals and ethical standards.

Challenges

• Lack of visibility and control over the AI lifecycle, from data collection to model deployment and monitoring
• Difficulty in ensuring the quality, performance, and robustness of the AI models against adversarial attacks, data drift, and changing requirements
• Complexity in managing the dependencies, configurations, and resources of the AI systems across different environments and platforms
• Risk of violating privacy, security, fairness, transparency, and accountability principles when using AI systems

Risks

EXPLAINING THE RISK

These challenges can result in costly errors, delays, inefficiencies, reputational damage, and legal liabilities for businesses that use AI products.

AIR Engineering leverages SRE practices

• Improved reliability and stability of AI and ML products: defining service level objectives (SLOs) and indicators (SLIs) for AI products based on business goals and user expectations
• Streamlined operations and maintenance: Implementing observability and monitoring tools to measure and track the SLIs and SLOs of AI products throughout their lifecycle
• Applying automation and testing techniques to ensure the reliability, security, and quality of AI products at every stage of development and deployment
• Faster development and deployment cycles: Establishing feedback loops and incident management processes to identify and resolve issues quickly and effectively
• Enhanced collaboration between AI, ML Dev teams, DevOps and SRE engineers: conducting postmortems and root cause analysis to learn from failures and improve continuously

Key Benefits

• Increased confidence and trust in their AI products by ensuring that they meet the desired quality, performance, robustness, security, and ethical standards
• Reduced costs and risks by avoiding errors, delays, inefficiencies, reputational damage, and legal liabilities caused by unreliable AI products
• Improved customer satisfaction and loyalty by delivering AI products that meet or exceed their expectations
• Enhanced innovation and agility by enabling faster and safer experimentation and iteration of AI products