The Gang of Four's 23 object-oriented patterns shaped how an entire generation of developers designed software. In the 2010s, cloud computing introduced patterns such as publish-subscribe ("pub-sub"), microservices, event-driven workflows, and serverless models, which now power most cloud-based distributed systems.
Similarly, before the current AI boom, the machine learning community had already developed "ML design patterns". When you build and deploy ML models, you face specific challenges, and patterns like Checkpointing, Feature Stores, and Versioning have become standard practice.
Why should you care about these patterns? They help you solve known problems in standardized ways. Instead of reinventing solutions, you use a shared vocabulary. When you say "Singleton", "Pub-Sub", or "Feature Store", your team immediately understands your approach. This speeds up your development, reduces errors, and makes your systems easier to maintain.
Modern AI systems present new challenges that neither classic software nor conventional machine learning (ML) patterns fully address.
For example, how do you guide model output and prevent misleading content? How do you build user experiences that help users understand, trust, and effectively use AI-powered applications? How do you manage agent interactions in multi-agent systems? How do you reduce computational costs to make your product sustainable?
Many AI patterns have emerged across the industry to help develop a well-architected AI system. The full version of this article demonstrates how existing patterns fit together, organized into five categories that build on each other as you scale your AI system.
- Prompting and Context Patterns to craft effective instructions and provide relevant context to guide the model's output
- Responsible AI Patterns to ensure ethical, fair, and trustworthy outputs
- User Experience Patterns to build intuitive interactions
- AI-Ops Patterns to manage AI at scale
- Optimization Patterns to maximize efficiency and reduce cost
This content is an excerpt from a recent InfoQ article by Rahul Suresh, "Beyond the Gang of Four: Practical Design Patterns for Modern AI Systems".
To get notifications when InfoQ publishes content on these topics, follow "AI, ML & Data Engineering", "Machine Learning", and "Large Language Models (LLMs)" on InfoQ.
Missed a newsletter? You can find all of the previous issues on InfoQ.
Sponsored
|
|
As APIs become core to digital architecture, sprawl, governance gaps, and performance issues can hinder agility and innovation. The APIOps Playbook provides architects with strategic guidance to operationalize API management at scale. Explore patterns for federated governance, built-in security, and streamlined API delivery that align with enterprise architecture goals. Learn how to evolve APIOps into a disciplined practice that enhances control, resilience, and developer productivity across complex, distributed systems.
Download the white paper “The APIOps Playbook: Managing APIs with Speed, Security, and Scale” sponsored by Boomi
|
|
Upcoming Events
QCon: For practitioners, by practitioners
InfoQ Dev Summit Boston 2025 (June 9–10): Last chance to register
If you're tackling complex development challenges and need real-world solutions from experienced practitioners, don’t miss this. In just two weeks, join senior developers for practical talks on AI/ML, modern architectures, performance, resilience, and DX. Explore the schedule and save your seat before it’s too late!
InfoQ Dev Summit Munich 2025 (October 15–16): Lead through the next wave of software change
Across Europe, senior developers are facing AI disruption, rising cloud complexity, and pressure from evolving EU regulations. Join peers for two days of practical talks on secure AI adoption, resilient systems, platform engineering, and regulatory strategy. See the schedule and save with early bird pricing through June 10.
QCon San Francisco 2025 (November 17–21): Navigate what’s next in software
From AI-enabled workflows to modern platform engineering and distributed architectures, software complexity is surging. Join senior engineers for 12 curated tracks and 60+ peer-driven sessions on scaling systems and making smarter tech decisions. Register now. Early bird pricing ends June 10.
QCon AI New York 2025 (December 16–17): Build AI systems that deliver
Move beyond prototypes. Learn how real teams are scaling AI across the SDLC, from architecture and integration to MLOps, compliance, and business impact. Two days of peer-driven talks focused on production-grade AI. Secure your spot. Early bird pricing ends June 10.
