Description
AI & ML Search with OpenSearch (elasticsearch + AI/ML) course. This comprehensive course teaches how to implement advanced search methods including semantic, hybrid, neural, and multi-modal using the open-source OpenSearch platform. OpenSearch, a fork of Elasticsearch, fully preserves the lexical search capabilities based on the BM25 algorithm, while allowing integration with large language models, providers such as OpenAI, and implementing agent-based workflows. The training content focuses on AI/ML use cases such as incremental retrieval-based production and migration from Elasticsearch, as well as traditional concepts for understanding historical context. The course relies on the production version of OpenSearch 2.17 and makes extensive use of Docker to ensure repeatability. The OpenSearch platform has gained a strong foothold in enterprise environments, with support from companies such as Oracle and the AWS cloud service, and its main components include OpenSearch, an alternative to Elasticsearch, Data Prepper, equivalent to Logstash, and OpenSearch Dashboards, similar to Kibana.
What you will learn
- Understand and implement traditional search, neural search, and hybrid search using Amazon OpenSearch, an Apache-licensed open source platform.
- Implement semantic search and retrieval-based augmented production (RAG) using locally hosted models or external LLM providers such as OpenAI.
- Deploy real-time projects entirely on a local machine or a cloud virtual machine (Cloud VM) using VS Code, Shell scripts, Python, and YAMl templates.
- Implement reporting, alerting, dashboards, observability log patterns while understanding integration points with the cloud.
- Completed multiple case studies, including migrating production data from Elasticsearch to OpenSearch.
- Understand and implement agent-based workflows including RAG architectures on local and external LLMs.
This course is suitable for people who:
- Undergraduate students without real-world project experience.
- Real-world experienced professionals from non-search (or even search) domains.
- Software Developer.
- DevOps Engineer / SysOps Admin / Site Reliability Engineer.
- Data Scientist / Analyst / Engineer.
- Engineers who are planning to make a lateral career change (towards search and AI/ML).
- Polyglot Engineers are passionate about saving costs and improving the performance of existing search platforms.
Course details
- Publisher: Udemy
- Instructor: Pradeep Macharla
- Training level: Beginner to advanced
- Training duration: 16 hours and 35 minutes
- Number of lessons: 74
Course syllabus in 2025/5
Prerequisites for the AI & ML Search with OpenSearch (elasticsearch + AI/ML) course
- 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 less using a public LLM service eg Open AI
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 720p
Download link
Downloadly
Rapidgator link
File(s) password: www.downloadly.ir
File size
9.2 GB
