AI & ML Search with OpenSearch (Elasticsearch AI/ML)
Course Details
Course Curriculum & Description
Master modern AI-powered search systems using OpenSearch and Elasticsearch. This practical course teaches how intelligent search engines work by combining traditional information retrieval with Machine Learning, vector embeddings, semantic search, and Retrieval-Augmented Generation (RAG).
You'll build real-world search applications using OpenSearch, explore vector databases, learn hybrid search techniques, implement AI embeddings, and integrate Large Language Models (LLMs) into search workflows. The course also covers OpenSearch agents, AI pipelines, dashboards, and production-ready search architectures.
Whether you're a software developer, AI engineer, data engineer, search engineer, or machine learning practitioner, this course provides the practical skills needed to build next-generation AI search applications.
Course Curriculum
Section 1: Introduction
- Course overview
- AI search fundamentals
- Setting up OpenSearch
Section 2: Embeddings & Vector Search
- Text embeddings
- Vector indexing
- Similarity search
Section 3: OpenSearch Concepts
- Search architecture
- Clusters and indexes
- Search APIs
Section 4: OpenSearch Techniques
- Full-text search
- Filters
- Ranking
- Relevance tuning
Section 5: OpenSearch Vector and AI Search
- Vector databases
- Embedding models
- Semantic retrieval
Section 6: OpenSearch Agents and RAG
- AI agents
- Retrieval-Augmented Generation
- LLM integration
Section 7: OpenSearch Dashboard
- Dashboards
- Monitoring
- Analytics
Section 8: OpenSearch AI Pipelines
- AI workflows
- Search automation
- Production deployment
Section 9: Final Project
- Build an AI-powered enterprise search application
Requirements
- Basic programming knowledge
- Familiarity with APIs
- Basic understanding of Machine Learning concepts (helpful)
- Internet connection
- No prior OpenSearch experience required
What You Will Learn
- After completing this course
- you'll be able to:
- Build enterprise AI search systems
- Implement semantic search
- Create vector databases
- Deploy OpenSearch clusters
- Generate embeddings for search
- Build Retrieval-Augmented Generation applications
- Integrate LLMs into search workflows
- Optimize search relevance
- Create AI-powered knowledge retrieval systems
- Develop production-ready search solutions
Course Highlights
Hands-on Projects
OpenSearch
Elasticsearch
Semantic Search
Vector Databases
AI Search
Retrieval-Augmented Generation (RAG)
LLM Integration
Enterprise Search
Production Deployment
Certificate of Completion
Lifetime Access