data/projects/ai_analytics_platform.md

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)