157 articles
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
I turned implicit processes into explicit operating rules—defining owners, acceptance tests, and lightweight runbooks so teams can move confidently and…
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
A unified platform for building, deploying, and managing AI applications and agents at enterprise scale.
In 2024, the community consensus was "RAG first, fine-tune never." In 2026, it's more nuanced.
Workflow: Predefined steps. AI handles specific tasks within a fixed pipeline. Deterministic flow.
After building production systems on both, here's the honest comparison. Enterprise compliance. Your data stays in your Azure tenant. No data sent to…
Vector search alone misses exact matches. Keyword search alone misses semantic similarity. Combine them.
Good API design is timeless. These patterns work because they prioritize developer experience and operational reliability.
Transformers solve a key problem: how do you process sequences while understanding relationships between all elements, not just adjacent ones?
Zero-trust isn't optional for AI workloads handling sensitive data. Implement these patterns from day one to avoid costly retrofitting.
Reliable messaging is the backbone of microservices architecture. Azure Service Bus provides enterprise-grade messaging, but using it effectively requires…
Choose Container Apps when: You want managed infrastructure Your team lacks Kubernetes expertise You have event driven or HTTP workloads You need rapid time…
Event-driven architecture requires careful design but delivers superior scalability and resilience for distributed systems.
The most successful AI implementations share common architectural patterns. Hybrid search combining vector and keyword retrieval consistently outperforms…
The best vector database depends on your specific requirements. Azure AI Search excels for hybrid search with integrated semantic ranking. Pinecone offers…
Multi-agent architectures shine for tasks requiring diverse expertise: research reports, code reviews, complex analysis, and creative projects. The key is…
At its core, an AI agent follows a simple loop: observe, think, act, repeat. Tools should be focused, well-documented, and handle errors gracefully.
Understanding june recap is essential for production AI systems. Here's what you need to know.
Understanding data lineage is essential for production AI systems. Here's what you need to know.
Understanding metadata management is essential for production AI systems. Here's what you need to know.
Understanding data governance is essential for production AI systems. Here's what you need to know.
Understanding unity catalog is essential for production AI systems. Here's what you need to know.
Understanding iceberg ai is essential for production AI systems. Here's what you need to know.
Understanding delta lake ai is essential for production AI systems. Here's what you need to know.
Understanding data lakehouse ai is essential for production AI systems. Here's what you need to know.
Understanding batch processing is essential for production AI systems. Here's what you need to know.
Understanding caching strategies is essential for production AI systems. Here's what you need to know.
Understanding load balancing ai is essential for production AI systems. Here's what you need to know.
Understanding model serving is essential for production AI systems. Here's what you need to know.
Understanding gpu optimization is essential for production AI systems. Here's what you need to know.
Understanding kubernetes ai is essential for production AI systems. Here's what you need to know.
Understanding container apps ai is essential for production AI systems. Here's what you need to know.
Understanding durable functions ai is essential for production AI systems. Here's what you need to know.
Understanding azure functions ai is essential for production AI systems. Here's what you need to know.
Understanding serverless ai is essential for production AI systems. Here's what you need to know.
Understanding azure event grid ai is essential for production AI systems. Here's what you need to know.
Understanding kafka ai is essential for production AI systems. Here's what you need to know.
Understanding event driven ai is essential for production AI systems. Here's what you need to know.
Understanding websocket ai is essential for production AI systems. Here's what you need to know.
Understanding streaming inference is essential for production AI systems. Here's what you need to know.
Understanding real time ai is essential for production AI systems. Here's what you need to know.
Sophisticated memory systems enable agents to learn and improve over time.
Choose the right execution pattern based on task complexity and agent capabilities.
Edge AI architecture balances latency, privacy, and capability across processing tiers.
Smart routing can reduce AI costs by 50-70% while maintaining quality.
The right model size depends on your specific requirements, not just capabilities.
A semantic layer enables AI to understand business terminology and generate accurate queries.
Data mesh with AI enables domain teams to own both data and intelligence.
Knowledge graphs add structured reasoning capabilities to AI applications.
Advanced RAG combines query expansion, hybrid retrieval, reranking, and context compression for better results.
Choose orchestration frameworks based on workflow complexity and team expertise.
Event-driven AI enables intelligent, automated responses to business events. Start with high-value events where AI can add immediate value.
Real-time AI requires discipline around latency. Design for the worst case and optimize for the common case.
The right model isn't always the biggest one. Match model capability to task requirements for optimal cost, latency, and quality.
Event-driven AI enables responsive, intelligent systems. Start with simple event handlers and evolve to complex orchestration as needed.
Best for: Enterprise search, RAG applications with complex filtering Best for: Global applications, multi-model data, transactional + vector workloads
AI-assisted data modeling accelerates the initial design phase but doesn't replace expertise. Use it to generate options quickly, then apply your domain…
These patterns form the building blocks of production AI applications. Combine them based on your specific requirements, and always include proper error…
The data platform of the future will feel like working with a knowledgeable colleague who understands your data and your business. Start building toward…
The data platform landscape is consolidating around unified, AI-native platforms with strong real-time capabilities. Organizations should align their…
Scaling AI Systems: From Prototype to Enterprise
The key to successful AI applications is choosing the right architecture for your requirements and building with production considerations from the start.
Microsoft Fabric represents the natural evolution toward unified, AI-powered data platforms. Understanding this evolution helps you make informed decisions…
A unified data platform on Fabric eliminates the complexity of managing multiple systems while providing the flexibility to handle any data workload.
HTAP in Microsoft Fabric enables unified transactional and analytical workloads without complex ETL pipelines. Design your schemas with both workloads in mind.
Agentic capabilities transform AI from a question-answering system into an autonomous problem-solver. The key is combining planning, tools, memory, and…
Circuit breakers are essential for AI systems that depend on external APIs. They prevent resource exhaustion, protect downstream services, and enable…
Graceful degradation is about providing the best possible experience given current constraints. Plan for every level of degradation and communicate clearly…
Fallback patterns ensure your AI application remains useful even when primary services fail. Design your fallbacks to maintain the best possible user…
Building reliable agents requires investment in error handling, recovery mechanisms, and observability. These patterns form the foundation for production AI…
For sub second latency: For minute level latency with relational sources: Combine streaming and batch for different data types: Distribute data to multiple…
1. Set appropriate thresholds : Not everything needs to be remembered 2. Consolidate regularly : Don't let short term memory overflow 3. Abstract…
Periodically consolidate memories for efficiency: 1. Layer your memory : Different types for different purposes 2. Manage capacity : Always have eviction…
Think of your agent as a state machine: Nodes : Processing steps (functions that transform state) Edges : Transitions between steps State : Accumulated…
The simplest approach: fixed rules based on task type. Pros: Simple, predictable, easy to debug Cons: Doesn't adapt, requires manual tuning
Different tasks need different capability levels. Using GPT-4o for simple classifications wastes money.
1. Clear domain boundaries Define ownership clearly 2. Product thinking Treat data as a product 3. Self serve enablement Empower domain teams 4.…
AI Agent Best Practices: Lessons from Production
Single agents have limits. Multi-agent systems multiply capabilities. Today I'm exploring architectures for agent collaboration.
Building single agents is one thing. Orchestrating multiple agents for complex workflows is another. Today I'm exploring production-ready orchestration…
AI agents are systems that can take actions autonomously to achieve goals. Today I'm exploring how to build agents from simple tool-using assistants to…
The choice between open-source and proprietary LLMs is one of the most important architectural decisions for AI projects. Let's analyze the tradeoffs…
1. Multi region deployment Distribute for resilience 2. Monitor continuously Track utilization and latency 3. Plan scaling windows PTU changes aren't…
Model selection should be systematic, not arbitrary. Define your requirements, score candidates objectively, and document decisions for future review.
Resilience : Failover when one provider has outages Cost optimization : Route to cheaper models when possible Capability matching : Use best model for each…
Eventstreams lets teams deliver streaming analytics without managing complex infra. From deployments I've supported, these patterns make ingestion and…
OneLake is central to Fabric's promise. My teams reorganised storage layouts and saw query performance improvements — these patterns capture what worked and…
Complex enterprise tasks benefit from specialization. In my work, coordinating small specialist agents led to clearer reasoning, easier testing, and more…
I've seen hundreds of RAG prototypes. The gap between a demo and a production-grade system usually comes down to retrieval quality, freshness, and…
Over the past year I've been part of several assistant projects that failed at scale not because the model was lacking, but because architecture and…
Having lived through the shift from monolithic warehouses to lakehouses, the pattern I'm seeing now is convergence: open formats, governance, and…
Fabric's component set is broad; the hard part is picking the minimal surface that solves your business need. This guide distils when to use Lakehouse…
Choosing Lakehouse vs Warehouse is an architectural trade-off: for open-format, large-scale analytics and ML, Lakehouse is the better fit; for classic T-SQL…
Designing for scale in Fabric is about decomposition and clear contracts between layers. The medallion architecture works well: small, composable…
Shipping Fabric into production exposes common anti-patterns — unbounded capacity use, duplicated data copies, and insufficient observability. The best…
After months of working with Azure OpenAI in enterprise environments, the patterns that separate solid deployments from problematic ones have become clear …
Design patterns and best practices for implementing content moderation in AI-powered applications.
Deep dive into LangChain's Runnable interface and its implementations for building flexible LLM pipelines.
Tomorrow we'll explore LangChain updates and new features. LangChain Memory Vector Stores Conversational Memory Patterns
Tomorrow we'll explore token estimation techniques. Rate Limiting Patterns Circuit Breaker Pattern Azure OpenAI Best Practices
This concludes our June 2023 series on Azure, Data, and AI topics. We covered: Microsoft Fabric deep dives (Lakehouse, Delta Lake, OneLake) Power BI Direct…
Fallback patterns ensure continuous service availability. Tomorrow, I will cover circuit breakers for AI applications.
Robust conversation management enables reliable production chat systems. Tomorrow, I will cover context pruning strategies.
Function calling, released last week with the 0613 model versions, gives AI agents a proper foundation — and I've been rebuilding some agent prototypes to…
AI orchestration patterns enable building sophisticated AI systems from modular components. The key is designing for reliability, observability, and…
These deployment patterns enable safe, controlled rollouts of LLM application changes. Start with gateway and blue-green patterns, then add canary and A/B…
Map-reduce transforms complex LLM tasks into manageable, parallelizable operations. Master these patterns for processing data at any scale.
The 32K model costs 2x more per token. Use it strategically. Effective context management balances quality, cost, and capability. Master these patterns to…
Deploy across regions for resilience: Configure Azure API Management for governance: Enterprise Azure OpenAI deployments need: 1. Multi region failover for…
Vectors (embeddings) represent meaning in high-dimensional space. Similar items have similar vectors.
The plugin ecosystem is just beginning. Now is the time to experiment and understand the patterns.
The basic pattern for straightforward use cases: Transform queries for better retrieval: Generate a hypothetical answer first, then use it for retrieval:…
RAG solves both by retrieving relevant context before generating responses.
The original API for text generation: Message based API with roles: Create an abstraction that works with both APIs:
Azure OpenAI uses Tokens Per Minute (TPM) as the primary quota metric: Model Default TPM Max TPM (with increase) GPT 3.5 Turbo 120K 300K+ Text Davinci 003…
The right choice depends on your specific requirements. For most enterprise scenarios, Azure OpenAI's security and compliance features make it the clear…
Until then, these patterns will help you build production-ready chat experiences.
2022 established these patterns as the foundation of modern data engineering. The lakehouse became the default architecture for new data platforms. Data…
The promise of serverless has largely been fulfilled: Pay only for what you use No server management Automatic scaling Faster time to market Most…
Lift and shift migrations VMs in the cloud Traditional architectures Using managed services Basic automation Some containerization Microservices…
Hybrid cloud strategies provide flexibility while meeting compliance and performance requirements.
Graph databases unlock powerful relationship-based queries that would be complex or impossible with traditional relational approaches.
This architecture provides a scalable, secure foundation for enterprise IoT solutions on Azure.
Handle intermittent connectivity by storing data locally and forwarding when connected. Reduce data volume by aggregating at the edge before cloud sync.
These patterns form the foundation for building scalable, performant IoT database solutions.
Implementing these HA patterns ensures your MySQL applications remain resilient during infrastructure failures.
Understanding this architecture helps you make informed decisions about data modeling and query optimization in your distributed PostgreSQL deployments.
1. Document all forwarding rules : Maintain a central registry 2. Use redundant DNS servers : Always specify multiple targets 3. Monitor DNS query latency :…
Private Endpoint : Consume services privately (you're the client) Private Link Service : Expose services privately (you're the provider) 1. SaaS providers :…
A well-planned environment strategy enables teams to move fast while maintaining governance and security.
Understanding solution layering prevents deployment issues and ensures predictable behavior across environments.
Regular Well-Architected Reviews ensure your workloads remain optimized across all five pillars as they evolve.
Azure Landing Zones provide the foundation for a successful enterprise cloud journey, ensuring consistency, security, and governance from day one.
Smart Spot instance architecture lets you achieve massive cost savings while maintaining the reliability your applications need.
Event-driven architecture in 2021 moved from architectural pattern to practical implementation. The tools support it, the patterns are proven, and the…
Log Analytics workspace design is one of those infrastructure decisions that feels low-stakes until your bill arrives or you hit a query that crosses a…
The Azure Functions Premium plan is the hosting option I reach for as soon as two of these three conditions are true: the function needs to connect to a…
Feature Consumption Premium Scaling 0 to 200 instances 1 to 100 instances Cold starts Yes No (pre warmed) Max timeout 10 min (default 5) 60 min (default 30)…
Use queues to handle variable load: Scale processing with multiple consumers: Process high priority items first: Coordinate long running business processes:…
Event Sourcing with Azure Cosmos DB provides a robust foundation for building systems that require complete audit trails, temporal queries, and event driven…
CQRS with Azure services enables building highly scalable applications with optimized read and write paths. Azure SQL provides transactional guarantees for…
The Azure Architecture Center is the reference resource I bookmark more than any other when I need to move from "I know what I want to build" to "I know the…
The Azure Well-Architected Framework is the document I send to clients at the start of every architecture engagement and the checklist I review before every…
Multi-tenant Azure management is the architecture problem I've spent more time on this year than any other. The patterns matter because the wrong choice…
The first Azure Function project I shipped used static everything. Static config, static SQL helpers, static logger. It worked—until I tried to write a unit…