1 min read
Model Fine-Tuning on Azure: When and How to Customize LLMs
Fine-tuning makes sense when you need consistent style, specialized terminology, or improved performance on specific tasks that prompt engineering cannot…
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Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 10 of 92
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Semantic Kernel: Orchestrating AI Agents with Plugins and Planners
Semantic Kernel abstracts the complexity of working with multiple AI services while providing extensibility through plugins. This allows developers to…
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Microsoft Ignite 2025 Preview: What to Expect for Azure AI
Ignite 2025 promises to be a pivotal event for enterprise AI adoption. The convergence of Azure AI, Fabric, and Copilot technologies creates opportunities…
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Real-Time Data Streaming with Microsoft Fabric Eventstreams
Batch processing introduces latency between data generation and insight availability. For fraud detection, operational monitoring, and customer engagement…
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Securing AI Applications: Authentication and Authorization Patterns
AI applications face unique security concerns: protecting training data, controlling model access, securing inference endpoints, and preventing prompt…
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Azure AI Search: Implementing Hybrid Search for Better RAG Results
Pure vector search excels at semantic similarity but can miss exact keyword matches. Pure keyword search finds exact terms but misses conceptually similar…
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Implementing Feature Stores with Microsoft Fabric OneLake
Without a feature store, teams often duplicate feature engineering work across projects. Features computed for training may differ from inference, causing…
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Prompt Engineering Patterns for Enterprise RAG Systems
Poorly designed prompts lead to hallucinations, ignored context, or responses that fail to meet business requirements. A systematic approach to prompt…
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Delta Lake Deep Dive: Time Travel and Schema Evolution in Fabric
Every write operation to a Delta table creates a new version. This transaction log enables point-in-time queries and rollback capabilities essential for…
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Testing LLM Applications: Strategies Beyond Traditional Unit Tests
LLM outputs vary between runs and model versions. Testing must focus on behavioral properties rather than exact string matching, while still catching…
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Optimizing GPU Utilization for AI Workloads on Azure
Many organizations run GPU workloads at 30-40% utilization, paying for idle compute. Understanding workload patterns and implementing optimization…
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Copilot Studio: Building Custom AI Agents for Enterprise Workflows
Traditional chatbots follow rigid conversation flows. AI agents powered by Copilot Studio understand intent, access knowledge bases, and execute multi-step…
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Implementing Data Quality Checks in Microsoft Fabric Lakehouses
Poor data quality leads to incorrect business decisions, failed ML models, and eroded trust in analytics platforms. A proactive approach to data validation…
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Building Production-Ready AI Pipelines with Azure Machine Learning
Many organizations struggle with the gap between experimentation and production. Models that work in notebooks often fail in real-world scenarios due to…
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Pre-Ignite Checklist: Preparing Your Organization for AI Announcements
Use this checklist to ensure your organization is ready to quickly evaluate and act on the AI innovations announced at Microsoft Ignite 2025.
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Batch Processing AI Workloads with Azure Batch and OpenAI
Track job progress and handle failures with proper retry logic for robust batch AI processing at scale.
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GraphRAG: Combining Knowledge Graphs with Retrieval-Augmented Generation
GraphRAG excels when your questions involve multi-hop reasoning, relationship discovery, or when entity connections matter more than document similarity.
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Testing AI Applications: Strategies for LLM-Based Systems
Test AI components within the full application context, including error handling, timeout behavior, and integration with downstream systems.
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Data Mesh Architecture: Enabling AI at Scale with Decentralized Data
Implement federated governance that balances domain autonomy with enterprise standards for AI-ready data products.
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Building Copilot Extensions with Azure OpenAI Assistants API
The Assistants API provides the foundation for building powerful, context-aware AI assistants that integrate with enterprise systems.
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Implementing Zero-Trust Security for AI Applications
Zero-Trust security ensures AI applications remain protected while enabling legitimate business use cases.
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Azure AI Speech: Building Voice-Enabled AI Applications
Voice interfaces make AI applications accessible to broader audiences and enable hands-free interaction scenarios.
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Implementing Retrieval-Augmented Generation with Microsoft Fabric
Use Fabric notebooks to create interactive RAG applications that combine data exploration with AI-powered question answering over enterprise datasets.
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Cost Optimization Strategies for Azure OpenAI Deployments
Balance cost and quality by routing requests to appropriate models based on task requirements.