1 min read
Enrichment Cache in Azure Cognitive Search
Enrichment cache significantly reduces costs and improves performance for AI-enriched search solutions.
Blog
Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 60 of 92
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Working with Projections in Azure Cognitive Search
Projections enable flexible storage and querying of AI-enriched content for various downstream use cases.
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In Memory SQLite database on C#
One of the advantages of using SQLite is the ability to treat the data store just like a SQL database. You can perform simple T-SQL query statements and…
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Knowledge Store in Azure Cognitive Search
Knowledge Store enables rich analytics and knowledge graph construction from AI-enriched content.
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Hybrid Search Patterns in Azure Cognitive Search
Hybrid search patterns enable building search systems that handle diverse query types effectively.
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Vector Search Preview in Azure Cognitive Search
Vector search enables powerful similarity-based retrieval that complements traditional keyword search.
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Semantic Search in Azure Cognitive Search
Semantic search dramatically improves search relevance by understanding user intent rather than just matching keywords.
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Azure Cognitive Search Updates and New Features
Azure Cognitive Search provides enterprise-grade search capabilities with AI enrichment options.
2 min read
Backup Azure SQL Database using Azure Data Studio on your Desktop
I'm trying to backup our SQL Azure database, and to my surprise, it doesn't seem to be "straight forward". Officially, you have to do it via the Azure…
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Automated Machine Learning with Azure ML AutoML
AutoML accelerates model development while maintaining production-quality results.
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Parallel Jobs in Azure ML for Large-Scale Processing
Parallel jobs enable processing datasets of any size by distributing work across your compute cluster.
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Hyperparameter Tuning with Azure ML Sweep Jobs
Sweep jobs automate the tedious process of hyperparameter tuning, helping you find optimal configurations faster.
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Building Reusable Components in Azure ML
Well-designed components enable team collaboration and accelerate ML development through reuse.
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Azure ML Pipelines v2: Modern ML Workflows
Azure ML Pipelines v2 provides a modern, Pythonic way to build production-ready ML workflows.
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Prediction Drift Monitoring for ML Models
Prediction drift monitoring provides early warning of model issues without waiting for ground truth labels.
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Feature Drift Monitoring for ML Systems
Feature-level drift monitoring enables targeted investigation and remediation of model issues.
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Concept Drift Detection in Machine Learning
Detecting concept drift enables timely model retraining to maintain prediction accuracy over time.
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Data Drift Detection for ML Models
Early drift detection enables proactive model maintenance and prevents silent failures in production.
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Model Monitoring in Production with Azure ML
Comprehensive monitoring ensures your ML models maintain their performance and reliability in production.
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A/B Testing for ML Models in Production
A/B testing provides statistical evidence for model selection based on actual business outcomes.
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Canary Deployments for ML Models
Canary deployment provides a controlled, observable approach to rolling out new model versions with minimal risk.
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Blue-Green Deployment for ML Models
Blue-green deployment provides a safe, zero-downtime approach to updating ML models in production.
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Azure ML Managed Online Endpoints for Model Deployment
Managed online endpoints simplify model deployment while providing enterprise-grade reliability and scalability.
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My Tech List 2022
Here is a list of all the relevant technology set up that I use personally at work, home, and everything in between. I grouped them on different groups…