AI & ML Search with OpenSearch (elasticsearch + AI/ML)
Год выпуска: 2025
Производитель: Udemy
Сайт производителя:
https://www.udemy.com/course/ai-ml-search-with-opensearch/
Автор: Pradeep Macharla
Продолжительность: ~16h35m
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание: 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
Содержание
06:51
09:34
08:31
Demo: Datasets | Projects | Course Material Downloads
06:59
System & Network Requirements | Configuration
09:35
Plugins | Upgrading
06:13
OS client libraries
04:57
10:01
12:52
11:15
10:43
What is OpenSearch | History | Capabilities | Related Projects
04:02
Types of Searches
09:38
Advanced Search Types
05:38
05:21
09:16
OS Architecture | Shards | Inverted Index | TF-IDF | BM25 Algorithm
11:22
Demo: ElasticSearch minimal
09:32
Demo: ES & OS minimal (continued)
22:11
Text Analysis Pipeline
04:09
Analyzer | Components | Use Cases | Built-in vs. Custom
09:53
Normalization Techniques | Tokenizers | Token filters
09:04
Demo: ES & OS minimal (continued)
11:21
Demo: Mappings
24:42
Demo mappings(continued)
26:26
Demo: Text Analysis
07:32
Search Methods | Search Query Languages | Search Performance | Search Relevance
05:48
Search Results | Highlight | Paginate | Sort | Collapse | AutoComplete
16:52
Retrieve Specific Fields | Keyword Search
10:00
Demo: Search Data
25:13
Demo: Search Data (continued)
15:04
kNN Search | kNN Index
07:34
15:15
AI | ML | LLM - Intuition & Analogies
23:08
Demo: Sentence Transformers
24:27
Demo: RAG | multi-modal
20:02
Vector Indexing
15:06
Demo: kNN Search
11:38
Demo: Neural Search
21:27
Demo: Neural Search (continued)
16:21
Demo: Edge Ngrams
06:53
ML Commons Plugin | Algorithms | Choosing and using LLM Models per OS jargon
22:48
Demo: OS pretrained sentence transformer model: msmarco distilbert
17:51
Demo: OS pretrained sentence transformer local ONNX
17:51
Demo: OS pretrained sentence transformer local TORCH
08:37
Demo: OS sentence transformers not registered local ONNX
05:47
Demo: OS supported pretrained SPARSE ENCODING
09:05
Demo: OS supported pretrained CROSS ENCODER
06:37
External Model | Remote Model Call | OpenAI | Demos
24:22
Connector Blue Prints | Pre & Post processing | Demos
20:14
12:06
Overview | Types of Agents | Demo Flow Agent (rag)
29:07
Demo: RAG with non-supported sentence transformers
10:11
Demo: RAG with interns dataset
19:07
Tools | Rerank
02:59
Demo: Rerank with cross encoding
18:40
21:24
Demo: RAG conversational flow agent with multiple knowledge bases aka. indices
07:00
Demo: RAG dynamic index selection
11:08
Demo: RAG chatbot conversation agent
19:23
Getting Started | Query Languages | Components
20:20
15:26
Demo: Analyzing Data
14:15
Demo: Creating Dashboards
08:34
Demo: Custom Branding
03:29
Demo: DQL
14:43
Demo: Observability | Integrations | Notebooks | Reporting | Search Relevance
13:40
Data Prepper | Use Cases | Concepts
08:41
Data Prepper | vs. Logstash | Sources | Processors | Sinks
07:52
Demo: Log ingestion Apache Logs
13:05
Demo: Log Ingestion Open Telemetry Logs
08:46
Project1: Load, create, and restore within OpenSearch
34:31
27:50
Project3: Geo Spatial Analysis
18:01
Файлы примеров: присутствуют
Формат видео: MP4
Видео: AVC, 1280x720, 16:9, 30fps, ~1500kbps
Аудио: AAC, 44.1kHz, 128kbps, stereo
