# AI Analytics Platform Project

## Project Overview
**Project Name**: Aurora Intelligence Platform  
**Duration**: 24 months  
**Team Size**: 15 engineers + 5 data scientists  
**Status**: Active Development  

## Technology Stack
- **ML/AI**: Python, TensorFlow, PyTorch, Scikit-learn, MLflow
- **Backend**: Python (FastAPI), Go microservices
- **Data**: Apache Spark, Apache Kafka, ClickHouse, MongoDB
- **Infrastructure**: Kubernetes, AWS (EKS, SageMaker, S3, EMR)
- **Frontend**: React, D3.js, Apache Superset
- **MLOps**: Kubeflow, Apache Airflow, DVC

## Architecture
- Event-streaming architecture for real-time analytics
- ML model serving with A/B testing capabilities
- Data lake architecture with Delta Lake
- Real-time and batch processing pipelines
- Multi-tenant SaaS platform

## Core Components

### 1. Data Ingestion Pipeline
- **Technologies**: Apache Kafka, Apache NiFi, AWS Kinesis
- **Capabilities**: Real-time streaming, Batch processing, Schema evolution
- **Data Sources**: APIs, Databases, Files, IoT devices

### 2. ML Model Training Platform
- **Technologies**: Kubeflow, MLflow, TensorFlow Extended (TFX)
- **Capabilities**: AutoML, Model versioning, Experiment tracking
- **Models**: Time series forecasting, Anomaly detection, NLP models

### 3. Real-time Analytics Engine
- **Technologies**: Apache Flink, ClickHouse, Redis
- **Capabilities**: Sub-second query response, Complex event processing
- **Use Cases**: Real-time dashboards, Alerting, Fraud detection

### 4. Model Serving Infrastructure
- **Technologies**: TensorFlow Serving, Seldon Core, NVIDIA Triton
- **Capabilities**: Auto-scaling, Multi-model serving, GPU acceleration
- **Features**: Canary deployments, Performance monitoring

## Skills Required by Role

### ML Engineer
- **Essential**: Python, TensorFlow/PyTorch, Statistics, Linear Algebra
- **Intermediate**: MLOps tools, Model deployment, Feature engineering
- **Advanced**: Distributed training, Model optimization, AutoML

### Data Engineer
- **Essential**: Python/Scala, SQL, Apache Spark, Kafka
- **Intermediate**: Data warehousing, ETL/ELT, Cloud platforms
- **Advanced**: Stream processing, Data governance, Performance tuning

### Backend Engineer (AI Platform)
- **Essential**: Python (FastAPI), Go, Microservices, APIs
- **Intermediate**: Docker, Kubernetes, Message queues
- **Advanced**: Distributed systems, Performance optimization, Security

### Frontend Engineer (Analytics)
- **Essential**: React, TypeScript, D3.js, Data visualization
- **Intermediate**: Real-time updates, Dashboard frameworks
- **Advanced**: Performance optimization, Complex visualizations

### DevOps/MLOps Engineer
- **Essential**: Kubernetes, Docker, CI/CD, AWS/GCP
- **Intermediate**: ML model deployment, Monitoring, Infrastructure as Code
- **Advanced**: ML pipeline automation, Model governance, Security

## Project-Specific Learning Paths

### ML Engineer Onboarding (6 weeks)
1. **Week 1**: Python for ML, NumPy, Pandas fundamentals
2. **Week 2**: TensorFlow/PyTorch basics, Model development
3. **Week 3**: MLflow experiment tracking, Model versioning
4. **Week 4**: Kubeflow pipelines, ML workflow automation
5. **Week 5**: Model serving, API development, Performance monitoring
6. **Week 6**: Production ML best practices, Model governance

### Data Engineer Growth Path (8 weeks)
1. **Week 1-2**: Apache Spark fundamentals, Distributed computing
2. **Week 3-4**: Stream processing with Kafka and Flink
3. **Week 5-6**: Data lake architecture, Delta Lake, Data governance
4. **Week 7-8**: Performance optimization, Monitoring, Troubleshooting

### MLOps Specialist Track (10 weeks)
1. **Week 1-2**: Kubernetes fundamentals, Container orchestration
2. **Week 3-4**: ML model deployment patterns, Serving infrastructure
3. **Week 5-6**: CI/CD for ML, Automated testing, Model validation
4. **Week 7-8**: Monitoring and observability, Model drift detection
5. **Week 9-10**: Advanced MLOps, Model governance, Security

## Current Technical Challenges
- **Model Drift Detection**: Implementing automated model retraining
- **Real-time Feature Engineering**: Low-latency feature computation
- **Multi-model Serving**: Efficient resource utilization
- **Data Quality**: Automated data validation and cleansing
- **Explainable AI**: Model interpretability for business users

## Open Positions
- Senior ML Engineer (TensorFlow/PyTorch)
- Data Platform Engineer (Spark/Kafka)
- MLOps Engineer (Kubernetes/AWS)
- Analytics Frontend Developer (React/D3.js)