End-to-End Engineering¶

Comprehensive guides and best practices for building robust, scalable data and AI systems. From data ingestion to model deployment and monitoring, we cover the full lifecycle of AI/ML projects.

Core Topics¶

Data Ingestion¶

• Batch Processing
• Stream Processing
• Data Validation

Data Processing¶

• ETL/ELT Pipelines
• Feature Engineering
• Data Quality

Model Development¶

• Experiment Tracking
• Model Training
• Hyperparameter Optimization

Deployment & Operations¶

• Model Serving
• Monitoring & Observability
• MLOps Best Practices

Getting Started¶

New to end-to-end ML systems? Start with our beginner's guide that walks you through building your first production-ready ML pipeline.

Case Studies¶

• Building a Real-time Recommendation System
• Scaling Computer Vision Models in Production
• Cost Optimization for NLP Workloads

Tools & Technologies¶

We cover popular tools in the ecosystem: - Data Processing: Spark, Dask, Ray - Workflow Orchestration: Airflow, Prefect, Dagster - Model Serving: TorchServe, TensorFlow Serving, BentoML - Monitoring: Prometheus, Grafana, Evidently

Contributing¶

Have experience with production ML systems? We welcome contributions! Check out our contribution guidelines to get started.