End-to-End AI Engineering

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

Getting Started

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

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

Hands-on AI Engineering Excercise

• LLMOps Healthcare Triage Series: Learn by Doing

Case Studies

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

Tools & Technologies

• 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 AI systems? We welcome contributions! Check out our contribution guidelines to get started.