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MLOps & Model Lifecycle Engineering

Productionize machine learning: feature stores, experiment registries (MLflow), automated drift detection, GPU inference scaling, and Triton serving.

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1 · Model Tracking & Registries

Artifact versioning, reproducible runs, MLflow, and lineage.

  1. 1. Experiment Tracking & Model RegistriesVersion controlling weights, datasets, hyperparameters, and production staging environments.
    9m · 2 MCQs

2 · Feature Stores & Pipelines

Online vs offline feature stores (Feast), time-travel joins, and training-serving skew.

  1. 2. Feature Stores & Mitigating Training-Serving SkewDual-layer stores (Redis + Snowflake), point-in-time correctness, and shared feature logic.
    10m · 2 MCQs

3 · High-Throughput Inference

Triton inference server, ONNX runtime, dynamic batching, and GPU acceleration.

  1. 3. GPU Inference Optimization & Dynamic BatchingTriton inference server, TensorRT quantization, and memory-bound vs compute-bound throughput.
    11m · 2 MCQs

4 · Drift & Retraining

KS-tests, Population Stability Index (PSI), concept drift, and automated retraining triggers.

  1. 4. Detecting Data & Concept Drift in ProductionKolmogorov-Smirnov tests, Population Stability Index (PSI), and automated retraining triggers.
    10m · 2 MCQs

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