This image illustrates the core pillars of ML System Engineering, outlining the journey from raw data to a responsible, deployed model.
- Data Engineering: Data Quality & Skew Prevention
- Focuses on building robust pipelines to ensure high-quality data. It aims to prevent “training-serving skew,” where the model performs well during training but fails in real-world production due to data inconsistencies.
- Model Optimization: Accuracy vs. Efficiency
- Involves balancing competing metrics such as model size, memory usage, latency, and accuracy. The goal is to optimize models to meet specific hardware constraints without sacrificing predictive performance.
- Training Infrastructure: Distributed Training & Convergence
- Highlights the technical backbone required to scale AI. It focuses on the seamless integration of hardware, data, and algorithms through distributed systems to ensure models converge efficiently and quickly.
- Deployment & Operations: MLOps & Edge-to-Cloud
- Covers the lifecycle of a model in production. MLOps ensures continuous adaptation and monitoring across various environments, from massive Cloud infrastructures to resource-constrained TinyML (edge) devices.
- Ethics & Governance: Fairness & Accountability
- Treats non-functional requirements like fairness, privacy, and transparency as core engineering priorities. It includes “fairness audits” to ensure the AI operates responsibly and remains accountable to its users.
Summary
- ML System Engineering bridges the gap between theoretical research and real-world production by focusing on data integrity and hardware-aware model optimization.
- It utilizes MLOps and distributed infrastructure to ensure scalable, continuous deployment across diverse environments, from the Cloud to the Edge.
- The framework establishes Ethics and Governance as fundamental engineering requirements to ensure AI systems are fair, transparent, and accountable.
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With Gemini
Lechuck Park 