Free Download AI & ML Search With OpenSearch (Intermediate level)
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 15h 42m | Size: 15.3 GB
Find meaning in your data with OpenSearch, Agents, AI and LLMs
What you'll learn
Understand how search results vary based on context-users may expect lexical matches or semantic understanding depending on query intent.
Build semantic search systems using dense vector embeddings and similarity metrics for intelligent information retrieval.
Design hybrid search solutions combining keyword and semantic approaches with score fusion techniques for optimal relevance.
Develop neural sparse search techniques for efficient and accurate sparse vector retrieval with advanced indexing.
Create agentic systems with tool integration, memory management, and multi-step reasoning for complex queries.
Implement RAG flows combining retrieval, context augmentation, and LLM generation for conversational AI applications.
Deploy advanced reranking pipelines using cross-encoders to enhance agentic decision-making and result quality.
Build real-world intelligent applications with optimization, monitoring, and agentic orchestration patterns for production deployment.
Requirements
Basics of running docker container, python programming basics, and eagerness to understand and unpack how search works
Local laptop with at least 8GB RAM (16GB preferable) and 4 CPU cores (8 preferable). Be ready to spend about $5 or lesser using a public LLM service
Optional: Since this is intermediate level course, we might skip fundamental concepts for e.g. various types of tokenizers and analyzers. Refer to "AI & ML Search with OpenSearch" course for such concepts (This course was published in Jan 2025)
Description
OpenSearch (originally a fork of Elasticsearch started in 2021) has gained immense popularity and adoption in open source, and enterprise communities with its Apache open source license and a Linux foundation project. While providing parity with all the lexical search capabilities of ElasticSearch, OpenSearch integrates with agentic frameworks, LLM models (e.g. sentence transformers) , providers like OpenAI, Cohere, Anthropic and defines agentic workflows. AI & ML Search with OpenSearch (intermediate level) course provides extensive learning material and going deeper into ingestion & search techniques , while implementing real search use cases like retrieval-augmented-generation (RAG), agentic workflows and migrating from Elasticsearch to OpenSearch. Emphasis has been laid on AI/ML use cases more than the traditional/lexical concepts. Basic concepts of opensearch like types of tokenizers, analyzers are skipped in this course (because they have been addressed extensively in the course AI & ML Search With Opensearch that was published in Jan 2025)To compare Elasticsearch (ELK stack) & OpenSearch, we can roughly equate the below:Elasticsearch ~ OpenSearchLogstash ~ Data PrepperKibana ~ OpenSearch Dashboards OpenSearch is a fast moving platform in terms of its releases and features. We will be using version 3.3.0 which is production-ready as of October 2025. Docker has been extensively used in the course to ensure execution reproducibility of the entire course code. I am excited to be your instructor and hoping you resonate the same excitement !
Who this course is for
Undergrad with no-real-world project experience
Real-world experienced professionals who have basic understanding of search concepts
Software Engineer
Software Engineer planning to switch careers laterally (towards search and AI/ML)
Enterprise engineers search for alternatives to Elasticsearch , but with open source license and similar or better features
Startup Engineers building search-based products or applications or platforms
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