LiveAI/ML
2024

AI-Powered Analytics Dashboard

Real-time analytics platform with machine learning predictions and interactive visualizations

Project Overview

The AI-Powered Analytics Dashboard is a comprehensive real-time analytics platform that combines traditional business intelligence with cutting-edge machine learning capabilities. Built for enterprises that need to process and visualize large volumes of data while gaining predictive insights.

The platform features an intuitive React-based frontend with interactive visualizations powered by D3.js, while the backend leverages Python and TensorFlow for ML model training and inference. The entire system is deployed on AWS using Kubernetes for orchestration and scalability.

Key achievements include reducing data processing time by 80%, improving prediction accuracy by 15%, and supporting 10,000+ concurrent users with sub-second response times.

Project Details

Duration:6 months
Role:Lead Developer
Status:Live

Technologies Used

ReactTypeScriptPythonTensorFlowD3.jsAWSPostgreSQLRedisDockerKubernetes

Key Features

ML-Powered Insights

Real-time predictions and anomaly detection using advanced machine learning models

Interactive Visualizations

Dynamic charts and graphs with real-time data updates and user interactions

Real-time Processing

Sub-second latency for data processing and visualization updates

Enterprise Security

Role-based access control, data encryption, and compliance with industry standards

Multi-user Support

Collaborative features with user management and permission controls

Scalable Architecture

Microservices architecture that scales to handle millions of data points

Performance Metrics

10M+
Data Points Processed
daily
5K+
Active Users
monthly
95.2%
Model Accuracy
average
<500ms
Response Time
average
99.9%
Uptime
availability
40%
Cost Reduction
vs traditional

Technical Challenges & Solutions

Real-time Data Processing

Implementing sub-second latency for live data streams while maintaining accuracy

Solution:Used Apache Kafka for stream processing and Redis for caching

ML Model Deployment

Deploying and managing multiple ML models in production with automatic scaling

Solution:Built custom model serving infrastructure using TensorFlow Serving and Kubernetes

Interactive Visualizations

Creating responsive and performant visualizations for large datasets

Solution:Implemented data virtualization and progressive loading with D3.js

Scalability

Handling millions of data points and concurrent users

Solution:Microservices architecture with horizontal scaling and load balancing

System Architecture

Frontend Layer

React + TypeScript
D3.js Visualizations
Real-time WebSocket

ML Processing Layer

TensorFlow Models
Python Backend
Model Serving

Infrastructure Layer

AWS Kubernetes
PostgreSQL + Redis
Load Balancing

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