Description
AI & ML Search With OpenSearch (Intermediate level) is a course on using OpenSearch to build intelligent search systems based on artificial intelligence and machine learning advances, published by Udemy Online Academy. You will learn how to effectively index and query data, implement full-text search features, and take your search experiences beyond basic keyword matching by integrating AI/machine learning components such as semantic search, relational tuning, and vector embeddings. The course covers key OpenSearch concepts such as index mapping, analyzers, scoring algorithms, and performance optimization, and then moves on to using machine learning models to improve relevance, understand user intent, and deliver smarter results.
The AI and Machine Learning Search with OpenSearch (Intermediate) course provides extensive training material and goes into deeper ingest and search techniques, while implementing real-world search use cases such as Retrieval Augmentation Generation (RAG), agent-driven workflows, and migrating from Elasticsearch to OpenSearch. The emphasis is more on AI/ML use cases than on traditional/null concepts. Core OpenSearch concepts such as tokenizers and analyzers are omitted from this course (as they are covered extensively in the AI and Machine Learning Search with OpenSearch course published in January 2025).
What you will learn in AI & ML Search With OpenSearch (Intermediate level):
- Understand how search results change based on context – users may expect lexical matching or semantic understanding depending on the 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.
- Build operating systems with tool integration, memory management, and multi-stage reasoning for complex queries.
- Implement RAG streams combining retrieval, context augmentation, and LLM generation for conversational AI applications.
- And…
Course specifications
Publisher: Udemy
Instructors: Pradeep Macharla
Language: English
Level: Intermediate
Number of Lessons: 67
Duration: 15 hours and 42 minutes
Course topics
AI & ML Search With OpenSearch (Intermediate level) Prerequisites
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)
Pictures
AI & ML Search With OpenSearch (Intermediate level) introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
15.2 GB
