Revise README for improved project documentation

README.md

@@ -1,18 +1,252 @@
-# ClickML - build MLOps workflow (just click, save and use)
+<div align="center">
 
-#### ClickML is a low-code/no-code platform that helps MLOps engineers and data teams to create end-to-end ML pipelines — from ETL to model training and deployment — all through a simple, click-based interface.
+# ClickML
+### End-to-End ML Lifecycle Platform
+![Static Badge](https://img.shields.io/badge/license-Apache--2.0-blue)
+![Static Badge](https://img.shields.io/badge/Build_ClickML_Docker_Images-passing-green?logo=github)
+![Static Badge](https://img.shields.io/badge/PostgreSQL-blue?logo=postgresql&logoColor=white)
+![Static Badge](https://img.shields.io/badge/FastAPI-white?logo=fastapi&logoColor=%23009688)
+---
+![Apache Airflow](https://img.shields.io/badge/Apache%20Airflow-017CEE?style=for-the-badge&logo=apacheairflow&logoColor=white)
+![PostgreSQL](https://img.shields.io/badge/PostgreSQL-316192?style=for-the-badge&logo=postgresql&logoColor=white)
+![Amazon S3](https://img.shields.io/badge/Amazon%20S3-FF9900?style=for-the-badge&logo=amazons3&logoColor=white)
+![GitHub Actions](https://img.shields.io/badge/GitHub%20Actions-2088FF?style=for-the-badge&logo=githubactions&logoColor=white)
+![Docker](https://img.shields.io/badge/Docker-2496ED?style=for-the-badge&logo=docker&logoColor=white)
+
+</div>
 
-## Architecture
+ClickML is a modular, full-stack MLOps platform that converts UI-based workflow actions into executable machine learning jobs. It manages the complete ML lifecycle — from data ingestion and preprocessing to pretraining, fine-tuning, quantization, registry tracking, and deployment. Designed for scalability, reproducibility, and hardware compatibility.
 
 <img width="6261" height="3824" alt="ClickMLPlatform" src="https://github.com/user-attachments/assets/237bff55-c5be-4d23-aeac-85875503a9e7" />
 
-## Features
 
--  Drag-and-drop pipeline builder
--  ETL pipeline execution (transform, clean, normalize)
--  Train ML models via dedicated ML backend
--  Model serialization and deployment (FastAPI endpoints)
--  View logs, metrics, and monitor deployed models
--  Modular architecture for easy scaling and team collaboration
+## Workflow in ClickML
+
+```mermaid
+flowchart TB
+    %% ── Storage Layer (top-left) ──────────────────────────────
+    subgraph STORAGE["Storage Layer"]
+        LogFiles[("Log Files\nPostgreSQL")]
+        ModelDB[("Model File\nDatabase (S3)")]
+        PlatformDB[("Platform-dependent\nDatabase (PostgreSQL)")]
+    end
+
+    subgraph USER_STORE["User Storage"]
+        UserData[("User Data")]
+        UserDatabase[("User Database")]
+    end
+
+    %% ── Auth ──────────────────────────────────────────────────
+    UserA(["User"])
+    UserB(["User"])
+    SignUp["Sign Up"]
+    Login["Login"]
+
+    UserA --> SignUp --> UserDatabase
+    UserB --> Login --> UserDatabase
+    UserDatabase --> |"Authenticate"| InteractionLayer
+
+    %% ── ETL Pipeline (top-center) ─────────────────────────────
+    subgraph ETL["Airflow – ETL Pipeline"]
+        RunDAG["Run DAG"]
+        Extract["Extract Data"]
+        Transform["Transform"]
+        Load["Load Data"]
+        Trigger1{{"Trigger"}}
+
+        RunDAG --> Extract --> Transform --> Load --> Trigger1
+    end
+
+    APIConfig["API's State\nEndpoint / Secret Key"] --> ETL
+    ETL --> |"Logs"| LogFiles
+    Trigger1 --> |"Database?"| DBCheck{{"DB?"}}
+    DBCheck --> |"Yes"| DataLake
+    DBCheck --> |"No – Fetch data"| DataLake
+
+    DataLake[("Data Lake /\nWarehouse")]
+
+    %% ── Interaction Layer ─────────────────────────────────────
+    subgraph InteractionLayer["Interaction Layer"]
+        direction TB
+        PipelineCreate["Data Pipeline Creation"]
+        MLPipeline["ML / DL Pipeline"]
+        ModelDeploy["Model Deployment"]
+    end
+
+    PipelineCreate --> |"Format (optional)\nTransform data format"| ETL
+    PipelineCreate --> |"Database: hostname,\npassword, dbname"| ETL
+    PipelineCreate --> |"Trigger Time"| ETL
+
+    MLPipeline --> ModelSelection["Model Selection"]
+    ModelDeploy --> Redeploy["Redeploy"]
+    ModelDeploy --> ModelFileSelection["Model File Selection"]
+
+    %% ── ML Training Pipeline (right) ─────────────────────────
+    subgraph TRAINING["ML Training Pipeline"]
+        direction TB
+        Trigger2{{"Trigger"}}
+        DataPreprocess["Data Preprocess"]
+        ModelTrain["Model Train"]
+        Evaluation["Evaluation"]
+        TestVal["Test Validation"]
+
+        Trigger2 --> DataPreprocess --> ModelTrain --> TestVal --> Evaluation
+    end
+
+    ModelSelection --> |"Model Type"| TRAINING
+    ModelSelection --> |"Parameters"| TRAINING
+    ModelSelection --> |"Input/Output Features"| TRAINING
+    DataLake --> |"Fetch Data"| TRAINING
+
+    Evaluation --> ModelReport["Model Report"]
+    Evaluation --> ModelPKL[("Model\n(.pkl) File")]
+    TRAINING --> |"Logs"| TrainingLogs[("Model Training\nLogs")]
+    ModelPKL --> |"Storing output files"| DataLake
+
+    %% ── Deployment (bottom-center) ────────────────────────────
+    ModelFileSelection --> |"Model File (.pkl)"| DeployFlow
+
+    subgraph DeployFlow["Deployment Flow"]
+        FastAPI["Create Fast API Server"]
+        EC2Deploy["Deploy on AWS EC2"]
+        OutputJob["Output – Server Job"]
+
+        FastAPI --> EC2Deploy --> OutputJob
+    end
+
+    DeployFlow --> |"Logs"| LogFiles
+
+    %% ── Infrastructure (bottom-left) ──────────────────────────
+    subgraph INFRA["Infrastructure (AWS)"]
+        Terminal[">_ Terminal\nssh -i print-key clickml@ec2-ip"]
+        EC2["EC2 Instance"]
+        RDS[("RDS")]
+        ClickMLDB[("clickml-database")]
+
+        Terminal --> |"connect@ssh username"| EC2
+        EC2 --> |"Insert username\n+ password"| RDS
+        RDS --> ClickMLDB
+    end
+
+    InteractionLayer --> |"Send Models"| ModelSelection
+    ModelPKL --> ModelFileSelection
+    STORAGE --> InteractionLayer
+
+    %% ── Styles ────────────────────────────────────────────────
+    classDef storage fill:#4a90d9,stroke:#2c5f8a,color:#fff
+    classDef process fill:#f9f3d9,stroke:#c8a84b,color:#333
+    classDef decision fill:#ffe0b2,stroke:#e65100,color:#333
+    classDef infra fill:#e8f5e9,stroke:#388e3c,color:#333
+    classDef io fill:#fce4ec,stroke:#c62828,color:#333
+
+    class LogFiles,ModelDB,PlatformDB,UserData,UserDatabase,DataLake,TrainingLogs,ModelPKL,ClickMLDB,RDS storage
+    class Extract,Transform,Load,RunDAG,DataPreprocess,ModelTrain,Evaluation,FastAPI,EC2Deploy,ModelSelection process
+    class Trigger1,Trigger2,DBCheck decision
+    class EC2,Terminal infra
+    class ModelReport,OutputJob io
+```
+
+---
+
+## Table of Contents
+
+- [System Architecture](#system-architecture)
+- [Vision](#vision)
+- [Objectives](#objectives)
+- [Core Capabilities](#core-capabilities)
+- [Example Workflow](#example-workflow-in-clickml)
+- [Why ClickML Stands Out](#why-clickml-stands-out)
+- [Future Roadmap](#future-roadmap)
+
+# System Architecture
+
+ClickML follows a modular microservice-style structure:
+
+
+---
+## Vision
+
+ClickML simplifies complex ML engineering workflows into structured, traceable pipelines without sacrificing flexibility or control.
+
+It is built for:
+- ML Engineers
+- AI Researchers
+- Data Engineers
+- Students building production-grade ML systems
 
 ---
+# Objectives
+- To allow users to create configurable ETL pipelines.
+- To automate pipeline scheduling using Apache Airflow.
+- To provide a no-code machine learning model creation interface.
+- To store processed and raw data in the user’s database.
+- To support model training for regression and classification problems.
+- To generate pickle files and comprehensive model reports.
+- To deploy ML models via API endpoints.
+- To create a robust frontend for seamless user interaction.
+
+# Core Capabilities
+
+## Data Governance & ETL Engine
+- Structured dataset ingestion
+- Data version tracking
+- Pipeline-based transformations
+- Validation & schema enforcement
+- Reproducible preprocessing jobs
+
+- <img width="959" height="446" alt="image" src="https://github.com/user-attachments/assets/c4a4643a-d59c-416c-a8cc-07b38055f10e" />
+---
+- <img width="1917" height="891" alt="image" src="https://github.com/user-attachments/assets/67405880-f876-4bd5-b4b9-ccdc8fac1945" />
+
+
+## Model Training Engine
+
+- Pretraining workflows
+- Supports Multiple Models:
+  - Linear Regression
+  - Random Forest Regression
+  - Decision Tree Regression
+  - Random Forest Classification
+  - Decision Tree Classification
+- Hyperparameter configuration via UI
+- Distributed training support (Docker-ready)
+- Training logs & metrics tracking
+
+<img width="1897" height="889" alt="image" src="https://github.com/user-attachments/assets/d327ad76-64f9-44b0-9006-6836475c1824" />
+
+## Deployment Layer
+- FastAPI-based inference endpoints
+- Containerized model serving
+- Production-ready deployment structure
+
+## Workflow Orchestration
+- Airflow-integrated job scheduling
+- Modular DAG execution
+- Background task management
+- Retry & failure handling
+
+
+<img width="1897" height="893" alt="image" src="https://github.com/user-attachments/assets/fa8b30f6-f456-4e38-826b-c216cea587c9" />
+
+---
+
+# Why ClickML Stands Out
+
+- Full ML lifecycle coverage
+- Built-in reproducibility
+- UI → executable pipeline conversion
+- Model lineage tracking
+- Registry-driven deployment
+- Modular & scalable architecture
+
+# Future Roadmap
+
+- RAG pipeline integration
+
+- LLM fine-tuning modules
+
+- Experiment tracking dashboard
+
+- Kubernetes deployment support
+
+- Multi-user workspace system