Everything in AI & ML, page 22
Describe the key stages of a typical ML lifecycle
It tests end-to-end systems thinking beyond notebook prototyping. Strong answers list: problem framing, data processing, model development, deployment, and monitoring with retraining. A red flag is skipping data validation or post-deployment observability.
Model Risk Management: The Immune System for Production Models
Model Risk Management treats every deployed model as a liability that can silently decay. Banks use it to stop bad predictions from becoming bad decisions. The footgun is treating validation as a one-time checkbox instead of continuous governance.
Centralized vs Decentralized ML Platforms
A centralized ML platform trades team autonomy for standardization, while decentralized platforms embed ML tooling inside product teams. Centralized suites drown in ticket queues; decentralized ones duplicate cost and security holes without strong governance.
Dynamic Fan-out/Fan-in Pipelines
Dynamic fan-out/fan-in spawns parallel tasks from runtime data, then gathers results. Use it when input counts vary, like processing a daily changing set of files. The footgun is a fan-in task that hangs waiting for branches lost to partial failure.
Parameterization: One Pipeline, Any Environment
Externalize every path, hyperparameter, and compute setting so one pipeline runs unchanged across dev, staging, and production. This enables reproducible experiments and safe CI/CD. The footgun is branch-per-environment repos that silently diverge.
Adversarial Validation: Detect Drift with a Classifier
Adversarial validation reframes drift detection as a classification problem: train a model to separate training rows from production rows. If it can tell them apart, your feature distributions have shifted.
Inference Health Checks: Traffic Gates, Not Heartbeats
An inference server's health check is a traffic gate, not a heartbeat. Kubernetes uses it to route requests only after the model is loaded. The footgun is probing the root path, which stays green even when the model has crashed or the GPU is wedged.
Right-Size Inference and Stop Paying for Idle GPUs
Instance right-sizing matches inference to the smallest hardware that serves it without choking. It matters when GPU endpoints idle at 10% utilization. The footgun is copying your training spec into production; inference rarely needs that memory or multi-GPU.
Why GPUs Dominate Neural Network Training
A GPU is a freight train, a CPU a race car: deep learning moves identical math across huge batches. GPUs win on transformers and CNNs. The footgun is using them for tiny models, where data transfer overhead eats the gains.
Docker Image vs. Container: Blueprint vs. Runtime
A Docker image is a read-only blueprint; a container is a live instance with a writable layer. You build an image once in CI and run many containers from it in production. The footgun is mutating a running container without updating the image recipe.
MLflow Models Standardize Deployment Packaging
MLflow Models wrap artifacts into a standard package so one pipeline serves sklearn or PyTorch without new deployment code. Teams ship experiments to REST endpoints without Dockerfiles per model. Missing dependency logging lets model load but fail to predict.
Experiment Run: The Immutable Training Receipt
An experiment run is an auto-generated log for one training job: it captures hyperparameters, metrics, code, and artifacts. Teams use runs to debug regressions and audit settings. The footgun is logging many metrics without versioning data so comparison fails.
Log Transformation: Compress the Long Tail
Log transformation compresses the long tail of skewed data so outliers cannot dominate loss. Use it for features like income or latency that span orders of magnitude. The footgun is blindly applying it to zeros or negatives, which destroys data.
Model Registry: Source of Truth for Deployed Models
A model registry is the source of truth for which trained model runs where, turning anonymous artifact files into versioned, staged assets. It matters when you deploy multiple models or need instant rollbacks.
Model Server: The MLOps Deployment Bridge
A model server bridges ML training and production, operationalizing models within your release cycle. Use it when models must become first-class CI/CD citizens. The footgun is treating deployment as a one-time handoff rather than repeatable infrastructure.
Shadow Deployment: Test Models on Real Traffic
Shadow deployment runs a new model on real traffic without serving its predictions, letting you catch data drift before users are affected. It is the safest production validation method, but teams often forget to monitor its latency and resource costs.
ETL: Extract, Transform, Load
ETL moves data through three phases from sources to containers. It handles one or more inputs and outputs via software that automates the process on recurring schedules or in batches. The footgun is defaulting to manual runs when automation is typical.
How does text guide Stable Diffusion via U-Net cross-attention?
Tests whether you know text embeddings condition the U-Net through cross-attention. Good answers explain that image features query text keys and values at every layer. Red flag: claiming the prompt is concatenated to the image latent.
What is GAN mode collapse, its causes, and two mitigations?
Define mode collapse as diversity loss to few modes; cite discriminator imbalance and lenient JS loss; give two fixes: WGAN and mini-batch discrimination.
Explain Denoising Diffusion models and forward/reverse processes.
This tests if you see diffusion as iterative latent generation, not GANs. Forward: add Gaussian noise over T steps until data is pure noise. Reverse: a network iteratively denoises random noise into data.
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