Training Overview
Disclaimer: This course is independently developed and not affiliated with Microsoft. It covers concepts and skills that closely align with the objectives of Microsoft’s AI-3022: Implement knowledge mining with Azure AI Search training, making it a strong preparatory or complementary learning experience.
Designing Intelligent Knowledge Mining Solutions with Azure AI Search
This One-Day intermediate-level custom training equips Data & AI professionals and technically curious learners to extract insights from diverse data using Azure AI Search, guiding them through hands-on modules to architect scalable solutions, enrich content with custom skills and semantic techniques, implement advanced features like vector search and reranking, and maintain high-performance search pipelines across enterprise environments.
Before enrolling in this custom training, it’s ideal for participants to have hands-on experience with Microsoft Azure, working knowledge of C# or Python, and a foundational understanding of AI and machine learning concepts. These skills will help learners confidently navigate the course’s practical modules, which focus on building scalable search solutions, enriching data with semantic techniques, and implementing advanced features like vector search and reranking.
Module Breakdown:
Create an Azure AI Search solution: Learn the core components of Azure AI Search, including indexing, filtering, and enhancing search results. Build a basic search solution from scratch.
Create a custom skill for Azure AI Search: Use AI-powered custom skills to enrich data during indexing, enabling deeper insights and tailored search experiences.
Create a knowledge store with Azure AI Search: Persist enriched data outputs for downstream analytics or integration with other systems.
Implement advanced search features: Improve relevance with ranking adjustments, term boosting, and multilingual search capabilities.
Search external data using Azure Data Factory: Integrate data from outside Azure into your search index using Azure Data Factory pipelines.
Maintain an Azure AI Search solution: Monitor and optimize performance, cost, and reliability of your deployed search solutions.
Perform search reranking with semantic ranking: Apply semantic ranking to improve result relevance using Level 2 (L2) ranking techniques.
Perform vector search and retrieval: Implement vector-based search to support semantic and similarity-based queries for modern AI applications.