Mlops
265 bites tagged Mlops — interview questions with model answers, and 60-second explainers.
Adversarial Attacks: Fooling Smart Models with Tiny Changes
Adversarial attacks trick ML models with tiny, imperceptible input changes, causing misclassification. It's like a visual illusion for an AI, turning a 'stop sign' into a 'speed limit' sign by altering a few pixels.
LIME: Explaining Single Predictions from Any ML Model
LIME explains a single prediction from any 'black box' model by approximating it with a simpler model that's only accurate locally. Use it to see why a specific user churned.
SHAP: Explaining Black Box Model Predictions
SHAP explains a model's prediction by treating features as players in a game and fairly distributing credit for the outcome. Use it to understand why a specific loan was denied or an image was misclassified. The footgun: SHAP explains the model, not reality.
ML Threat Modeling: Assume Your Data Is Compromised
Threat modeling for ML means assuming your training data is already compromised. This is crucial for services using public or user-supplied datasets. The main footgun is trusting data sources, as data poisoning can silently corrupt your model's behavior.
Model Interpretability vs. Explainability
Interpretability means a human can grasp a model's logic (e.g., a simple decision tree). Explainability is stronger: it's about why the model made a *specific* choice. This is key for debugging or justifying high-stakes decisions.
RBAC for MLOps: Who Can Do What?
RBAC assigns permissions to roles, not people. You create roles like 'Data Scientist' with specific permissions (e.g., access training data), then assign users to that role.
Hybrid Cloud MLOps: Train Anywhere, Deploy Everywhere
Treat your ML infrastructure like your applications—a consistent platform that runs anywhere, avoiding siloed stacks for data science and app dev. Use it to train on cloud GPUs but deploy on-prem for low latency, ensuring dev/prod parity across environments.
Compute Abstraction Layer: Run Code Anywhere
A Compute Abstraction Layer is a universal adapter for your code, letting you run it on a laptop, cloud GPU, or cluster without changes. It's used in MLOps to scale a script from local debug to production training. The footgun is a leaky abstraction.
The MLOps Maturity Model: A Roadmap for Growth
The MLOps Maturity Model is a roadmap from manual chaos to automated ML systems. Use it to assess your team's current state and plan incremental improvements.
MLaaS: Your Machine Learning Lab in the Cloud
Machine Learning as a Service (MLaaS) provides the key ingredients for ML—data, compute, and expertise—as a cloud service. This lets teams build models for forecasting or spam detection without buying expensive hardware.
PaaS: The Managed Platform for Building Applications
PaaS gives you a ready-to-use development environment, handling the OS and middleware so you can just code. It's used to accelerate app development for web, IoT, or ML. The main footgun is vendor lock-in, making future platform migrations difficult.
Pipeline Step Caching: Don't Recompute What You Don't Have To
Pipeline step caching is memoization for your ML infrastructure, saving time and money by reusing previous results. It's used in MLOps pipelines when inputs and code haven't changed. The footgun: the cache is scoped to one pipeline and a timeout, not globally.
Argo Workflows: Run Complex Jobs on Kubernetes
Think of Argo Workflows as a script runner for Kubernetes, where each command is a container. It runs multi-step jobs like CI/CD or ML pipelines. The footgun is treating it like a full CI server; it's just an engine and lacks features like Git polling.
Vertex AI Pipelines: Orchestrating ML Workflows
Think of it as an assembly line for your machine learning models, automating everything from data prep to deployment. Use it to build reproducible, production-grade ML systems on Google Cloud.
Amazon SageMaker Pipelines: Repeatable ML Workflows
Think of SageMaker Pipelines as a CI/CD pipeline for ML models, automating workflows from data prep to deployment. Use it for reproducible training and automated retraining.
TensorFlow Extended (TFX): Production ML Pipelines
TFX is an end-to-end platform for building production ML pipelines, like an assembly line for your models. It automates data validation, training, analysis, and serving. The footgun: TFX is not one tool, but a suite of libraries you must learn and connect.
DDM: Detecting Drift with Error Rate Statistics
DDM acts as a statistical alarm on your model's error rate, watching for spikes that signal the underlying data has changed. Use it for online binary classification with immediate feedback, like spam filtering.
Logging Model Explanations, Not Just Predictions
Log *why* your model makes a prediction, not just the output. This captures the model's reasoning, creating a debuggable audit trail. It's essential for diagnosing model drift and ensuring fairness.
Population Stability Index (PSI): Quantifying Data Drift
The Population Stability Index (PSI) gives you a single number to quantify data drift between training and live data. It's used in MLOps to monitor model health, especially in finance. The footgun is ignoring a high PSI, which signals silent prediction decay.
ML Monitoring Dashboards: Your Model's Health Chart
An ML monitoring dashboard is a health chart for your production model, showing how its performance decays. It tracks silent failures like data drift or concept drift, where user behavior changes and makes your model obsolete.
Inference Throughput: How Many Predictions Per Second?
Inference throughput measures how many predictions your system can make per second, not how fast a single one is. It's the system's total capacity, critical for high-volume tasks like recommendation engines. The footgun is confusing it with latency.
Multi-Model Serving: Packing More Models into Less RAM
Multi-model serving is a carpool for your ML models. Instead of one server per model, you pack many into a single process to share resources and cut costs. It's ideal for serving many models with intermittent traffic.
Model Compilation: Bridging Models and Hardware
An ML compiler translates a model's abstract math into optimized instructions for specific hardware. This lets you run the same model efficiently on cloud GPUs, mobile CPUs, or edge devices.
Model Pruning: Making ML Models Smaller and Faster
Model pruning is like trimming a bonsai tree; you remove the least important weights to create a smaller, faster model. It's essential for running large models on devices like smartphones, but over-pruning can irreversibly damage accuracy.
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