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Data ingestion + validation, distributed training (data vs model parallelism), experiment tracking, hyperparameter search, checkpointing + fault tolerance on long runs, the model-registry handoff to serving, reproducibility, and the economics of GPU-cluster utilization.
Design the platform that takes raw data and turns it into trained, versioned, deployable models - for dozens of ML teams running everything from a 20-minute gradient-boosted-tree job to a multi-week distributed training run on hundreds of GPUs. It must ingest and validate data, run training reliably (including runs long enough that hardware will fail mid-run), track experiments so results are comparable and reproducible, search hyperparameters, and hand finished models off to serving through a registry.
This is the "MLOps platform" question - the counterpart to the serving-platform problem, on the training side. Weak answers describe "a script that trains a model." Strong answers treat training at scale as a distributed-systems problem: fault tolerance on long runs (a week-long job on 256 GPUs will lose a node), the economics of keeping a very expensive GPU cluster utilized, reproducibility as a first-class requirement, and the clean handoff to serving that closes the loop with the feature store and the serving platform.
Asking these before diving into a solution is the difference between a "hire" and a "no signal" rating. Pick the questions whose answers would change your design.
Capacity estimation · architecture with all 8 components explained · 5 deep dives · trade-off analysis · 8 common follow-up questions
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Online vs batch inference, GPU utilization tricks, autoscaling for spiky load, A/B testing models, and the feature store that decouples training from serving.
The offline/online store split, train/serve skew as the core problem it exists to solve, point-in-time-correct joins, materialization and freshness, and a registry that lets teams reuse features instead of re-deriving them badly.
Batch vs streaming, lambda vs kappa, the warehouse-vs-lakehouse decision, and dimension modeling that survives schema drift.
Namespace vs bytes, erasure coding vs replication, the eleven-nines durability math, multipart upload, and why LIST is the hardest API in the system.
Reading is the floor. The interview signal is in walking through this live with someone probing follow-ups. Use the AI mock interview to practice talking through requirements, architecture, and trade-offs out loud.
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