Intelligent Agents

Simple Reflex
agents

Goal-based
agents

Utility-based
agents

Model-based
Reflex agents

Learning agents
Can modify their behavior based on past experiences and feedback, learning from the environment to make better decisions.

RL-based agents

LLM-based agents

Makes decisions by evaluating the
potential outcomes of its actions
and choosing the one that maximizes
overall utility.

Act to achieve specific goals, using the
model of the world to consider the
future consequences of their actions

Use the current state of the world
& the internal model of that world,
to decide on the best action

Uses a set of condition-action rules
coded into the system to make its
decision or take any action

PEFT Methods

Additive
Fine-Tunning

Adapter-based

Adapter Design

Serial Adapter, Parallel Adapter, CoDA

Multi-task Adaptation

AdapterFusion , AdaMix , AdapterSoup , MerA , Hyperformer

Soft Prompt-based

Soft Prompt Design

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

Training Speedup

Reparameterized
Fine-Tuning

Low-rank
Decomposition

LoRA , Compacter KronA , VeRA , DoRA

LoRA Derivatives

Dynamic Rank

DyLoRA , AdaLoRA , SoRA , CapaBoost , AutoLoRA

LoRA Improvement

Laplace-LoRA , LoRA Dropout , LoRA+

Multiple LoRA

LoRAHub, MoLORA , MoA, MoLE, MixLoRA

Selective
Fine-Tunning

Unstructural Masking

U-Diff pruning , FishMask , Fish-Dip , AM

Structural Masking

S-Diff pruning ,S-BitFit , FAR, Bitfit, SPT

Learn a policy that guides the agent
to take actions in different states to
maximize cumulative rewards

LLMs Tasks

Language Modeling

Text Generation

DataOps

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

Data Sorces

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

Product

2. Product Meta , Details, Categories

3. User Reviews

4. User clickstream data, system log, user preference

5. QA interactions (Bellysafe, Expert, ...) Text-based data.

6. Calendar Tracking: Time series data (appointments, events, etc.).

7. External, Knowledge_Base

Ingestion Layer

Batch ingestion: large data dumps like historical product data or user logs

Real-time ingestion: For handling QA, user chats , calendar event updates

Data Storage / Data Lake

AWS S3: suitable for building a data lake.

OpenSearch: If search and retrieval are key functions, OpenSearch (for analytics) can be used in combination with a data lake.

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

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

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

Product Use Cases

Product Similarity/relevancy

Rrecommendation

Category Rec

Popularity_based

Personalized

Product Basket

Customers Say

Summarization

Sentiment Analysis

Attributes/Tag generation

AI Criteria Tags

Search + Result Page

Filter + Result Page

Search

Semantic

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