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Keep shoppingAI & ML Search with OpenSearch (elasticsearch + AI/ML)
435 students
AI & ML Search with OpenSearch (elasticsearch + AI/ML)
Find the meaning in your data with OpenSearch & AI
Created byPradeep Macharla
Last updated 1/2026
English
What you'll learn
- Understand and implement traditional search, neural search, hybrid search using Amazon's OpenSearch, apache-licensed open-source platform
- Implement semantic search, retrieval augmented generation (RAG) using locally hosted models or external LLM providers like OpenAI
- Implement real-time projects entirely on a local machine or a cloud VM using VS code, shell scripts, python and yaml templates
- Implement reporting, alerting , dashboards, observability log patterns while understanding integration points with cloud
- Complete multiple case studies, including migration of production data from elasticsearch to opensearch
- Understand and implement agentic workflows involving RAG architectures on local and external LLMs
Explore related topics
Course content
11 sections • 78 lectures • 17h 50m total length
Requirements
- Basics of running docker container, python programming basics, and eagerness to understand and unpack how search works
- Local laptop with at least 4GB RAM (8GB preferable) and 2 CPU cores (4 preferable). Be ready to spend about $5 or lesser using a public LLM service e.g. Open AI
Description
Elasticsearch is a well-known search platform adopted in enterprises, SMBs and startups. Elasticsearch excels at lexical search use cases using BM25 algorithm , that is built on top of Lucene. However, with the advent of AI and large language models, Semantic Search, Hybrid Search, Neural Search, Multi-modal search etc. have become more of a norm than rarity.
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 LLM models (e.g. sentence transformers) , providers like OpenAI, Cohere, Anthropic and defines agentic workflows. As a win, Oracle switched to OpenSearch for its PeopleSoft search capabilities. AWS provides Opensearch-as-a-service on its cloud and that already speaks to the production readiness.
AI & ML Search with OpenSearch course provides end-end training on installing, configuring and understanding OpenSearch , 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, though the latter is covered for historical context.
To compare Elasticsearch (ELK stack) & OpenSearch, we can roughly equate the below:
Elasticsearch ~ OpenSearch
Logstash ~ Data Prepper
Kibana ~ OpenSearch Dashboards
OpenSearch is a fast moving platform in terms of its releases and features. We will be using version 2.17 which is production-ready as of September 2024. 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 from non-search domains (or search too)
- Software Developer
- Devops Engineer / SysOps admin / Site Reliability Engineer
- Data Scientist / Analyst / Engineer
- Engineer planning to switch careers laterally (towards search and AI/ML)
- Polyglot engineers eager to save costs , improve performance of existing search platforms