Luokat: Kaikki - personalization - modeling - integration - recommendation

jonka Eric Nic 1 päivä sitten

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SM Challenges

The content covers a diverse range of applications for AI and machine learning in product development and health recommendations. It includes processes such as data preparation, cleaning, and preprocessing of information sourced from Amazon.

SM Challenges

Product Use Cases

Search

FullText
Semantic

AI Criteria Tags

Filter + Result Page
Search + Result Page

Customers Say

Attributes/Tag generation
Sentiment Analysis
Summarization

Rrecommendation

Product Basket
Personalized
Popularity_based
Category Rec

Product Similarity/relevancy

DataOps

Security & Compliance: Ensuring data privacy (important for health-related apps) and compliance with regulations like HIPAA.

Monitoring and Quality Assurance: Ensure data quality, detect anomalies, and apply corrective actions.

Version Control & Collaboration: Managing different versions of data, changes in ML models, and collaboration among team members.

Data Pipeline Orchestration Managing the flow from data ingestion to storage and processing.

Data Storage / Data Lake
OpenSearch: If search and retrieval are key functions, OpenSearch (for analytics) can be used in combination with a data lake.
AWS S3: suitable for building a data lake.
Ingestion Layer
Real-time ingestion: For handling QA, user chats , calendar event updates
Batch ingestion: large data dumps like historical product data or user logs
Data Sorces
7. External, Knowledge_Base
6. Calendar Tracking: Time series data (appointments, events, etc.).
5. QA interactions (Bellysafe, Expert, ...) Text-based data.
Product

4. User clickstream data, system log, user preference

3. User Reviews

2. Product Meta , Details, Categories

1. Base data: User, Expert, ...tabular , text , geospatial

LLMs Tasks

Text Generation

Language Modeling

PEFT Methods

Selective Fine-Tunning

Structural Masking
S-Diff pruning ,S-BitFit , FAR, Bitfit, SPT
Unstructural Masking
U-Diff pruning , FishMask , Fish-Dip , AM

Reparameterized Fine-Tuning

LoRA Derivatives
Multiple LoRA

LoRAHub, MoLORA , MoA, MoLE, MixLoRA

LoRA Improvement

Laplace-LoRA , LoRA Dropout , LoRA+

Dynamic Rank

DyLoRA , AdaLoRA , SoRA , CapaBoost , AutoLoRA

Low-rank Decomposition
LoRA , Compacter KronA , VeRA , DoRA

Additive Fine-Tunning

Soft Prompt-based
Training Speedup
Soft Prompt Design

Prefix-tuning , p-tuning , Prompt-tuning , Xprompt, APrompt

Adapter-based
Multi-task Adaptation

AdapterFusion , AdaMix , AdapterSoup , MerA , Hyperformer

Adapter Design

Serial Adapter, Parallel Adapter, CoDA

SM Challenges

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