Microsoft Fabric Capacity Planning: F SKUs, Workloads and Cost Optimization
Plan Microsoft Fabric capacity from workloads, concurrency and service targets. Compare F SKU assumptions, CU measurements, smoothing, cost and saturation checks.
Fine-Tuning LLMs with LoRA and QLoRA: Architecture, Data Quality and GPU Cost
Compare LoRA and QLoRA architecture, dataset quality, training memory and GPU cost. Includes adapter configuration, evaluation gates and deployment checks.
AI Model Drift Detection: Data Drift vs Concept Drift and Monitoring Techniques
Distinguish data drift from concept drift, design monitoring windows and investigate alerts before retraining. Includes a transparent distance example and production checklist.
Voice AI Agents Architecture: Telephony, Streaming ASR, LLM and Text-to-Speech
Design voice AI agents with telephony, streaming recognition, LLM orchestration and speech output. Includes interruption handling, tool controls, handoff and release tests.
Speaker Diarization Explained: Models, Accuracy Metrics and Production Challenges
Understand speaker diarization, DER components, overlap and attribution failures. Includes a pipeline, evaluation checklist and production error taxonomy.
Speech-to-Text Architecture: Real-Time vs Batch Transcription for Contact Centers
Compare real-time and batch transcription for contact centers, including streaming latency, diarization, scheduling, protected recordings and evaluation checks.
Small Language Models vs Large Language Models for Enterprise AI
Compare small and large language models for enterprise tasks: domain quality, deployment, privacy, latency and total cost, with a practical decision flow.
LLM Inference Cost Optimization: Model Routing, Batching and Semantic Caching
Optimize inference with evaluated model routing, suitable batching and safe caching. Includes a token-cost calculation, explicit assumptions and benchmark worksheet.
How to Test LLM Applications: Regression Suites, Golden Datasets and Red Teaming
Build LLM regression suites with golden datasets, reusable cases, failure categories and controlled red teaming. Includes a practical release scorecard.
LLM Guardrails Explained: Input Validation, Output Filtering and Policy Enforcement
Compare LLM guardrail rules, classifiers and model reviewers. Includes a runnable output validator, action-policy boundaries and a practical testing checklist.