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-# ClickML - build MLOps workflow (just click, save and use)
+
-#### 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
+
+
+
+
+---
+
+
+
+
+
+
+
-## 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.
-## 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
+
+- 
+---
+- 
+
+
+## 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
+
+
+
+## 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
+
+
+
+
+---
+
+# 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