The ML Engineer track covers model training pipelines, feature engineering, experiment tracking, model deployment and serving, MLOps, and production ML reliability. Sessions calibrate by level and probe the depth of your design decisions.
The ML Engineer track covers training pipeline design, feature engineering and feature stores, model evaluation, A/B testing and deployment strategies, MLOps tooling, model monitoring, and behavioral questions on shipping ML products at scale.
The ML Engineer track focuses more on classical and deep learning model development, training infrastructure, and MLOps. The AI Engineer track focuses more on LLM integration, RAG systems, and applied AI product development. Both are distinct tracks with separate question banks.
Yes. The ML Engineer track includes MLOps-specific coverage including CI/CD for ML, model versioning, feature drift detection, retraining triggers, and production monitoring. There is also a dedicated MLOps Engineer track for candidates targeting platform roles.
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