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| # 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) | |