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Week 1

• Creating multiple fields is also helpful if you want to use a field for autocompletion, search-as-you-type, or joins.
• If you only need a field for ranking, you might consider using the Rank Feature field and query for improved performance.
• build a more sophisticated query by adding and grouping different types of queries via things like the “bool” query.
• Mappings : Take an iterative approach to field mappings. That is, start by indexing the data using a subset of the content and the default settings. Then look to see what OpenSearch guessed for mappings and then modify those values accordingly to the requirements above and your insights.
• Look for additional fields we could either search or leverage in our query. Eg: manufacturer, color that satisifes the user intent.
• Using function query to impleme

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## Altering resuts using painless - https://www.elastic.co/guide/en/elasticsearch/painless/7.10/painless-walkthrough.html

### Script score on price for doc 1

### In these results, doc_a gets a score of price (5.99) + 1 (due to the match_all score) = 6.99. All else gets a score of 1 (match_all score) + 1 (the non “doc_a” case in the script) = 2.

POST searchml_test/_search
{
  "query": {"match_all": {}},
  "rescore": {

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# Rescoring 

## Delete the index
DELETE /searchml_test


##Create our index
PUT /searchml_test
{
  "settings": {

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Introduction

For some general background, read the Introduction of Büttcher et al's IR textbook: in particular, 1.1, 1.2, and 1.4.

• 1.4 Test Collection
  • 1.4.1 TREC Tasks - TREC2 (Text REtrieval Conference), a series of experimental evaluation efforts conducted annually. TREC has included tracks devoted to enterprise search, genomic information retrieval, legal discovery, e-mail spam filtering, and blog search. Provides reusable test collections to validate the improvements.
• IR application 1) Web Search, Desktop Search or Intranet Search, Site Search 2) Text Clustering & Categorization 3) Summarization 4) Text Extraction 5) Topic Detection 6) Expert Search Systems - identifies the members who are experts 7) Question & Answering 8 ) Multimedia ir - video, image, music, speech
• IR System Architecture
• Performance Evaluation
• Efficiency : 1) Latency 2) Throughput 3) Space

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Benchmarks

Natural Language Understanding Systems

• GLUE : General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. This is a benchmark of nine sentence- or sentence-pair language understanding tasks.

Multitask Challenge for NLP

• decaNLP: Natural Language Decathlon, a new benchmark for studying general NLP models that can perform a variety of complex, natural language tasks. By requiring a single system to perform ten disparate natural language tasks, decaNLP offers a unique setting for multitask, transfer, and continual learning.

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MLOps Basics

Configurations

• Use Hydra to add configurations to python code.
• Loading a simple config(yaml) file using OmegaConf and Hydra
• Overriding configurations at runtime
• Splitting the configuration across multiple files
• Variable Interpolation
• How to run the model with different parameter combinations