Machine Learning to Inference Optimization

Represent observations, features, and model parameters as vectors, matrices, and tensors.

Compute dot products, matrix products, norms, and transposes used by linear models.

Calculate derivatives and partial derivatives of elementary loss functions.

Apply the chain rule to a small composition of differentiable functions.

Perform gradient-descent updates and diagnose the effect of learning-rate choices.

Calculate probabilities using conditional probability and Bayes’ rule.

Compute expectation, variance, covariance, and correlation from data.

Relate likelihood, log-likelihood, and loss functions for a simple statistical model.

Translate a product problem into features, targets, constraints, and a supervised-learning task.

Create train, validation, and test splits while preventing common forms of data leakage.

Build a scikit-learn preprocessing pipeline for mixed numerical and categorical features.

Fit and interpret a linear regression model as a predictive baseline.

Evaluate regression predictions with MAE, RMSE, R-squared, and residual plots.

Apply L1 and L2 regularization to control linear-model complexity.

Diagnose underfitting and overfitting with validation results and learning curves.

Record a reproducible baseline experiment with parameters, metrics, and artifacts.

Train logistic regression and interpret its output as class probabilities.

Evaluate classifiers with confusion matrices, precision, recall, F1, ROC-AUC, and PR-AUC.

Select a classification threshold from explicit error-cost or precision-recall requirements.

Train and constrain a decision tree to reduce overfitting.

Train a random forest and evaluate its improvement over a single tree.

Train a gradient-boosted tree model for tabular classification.

Tune model hyperparameters with cross-validation without contaminating the test set.

Apply class weighting or resampling to an imbalanced classification problem.

Encode categorical variables while avoiding target leakage and invalid ordinal assumptions.

Scale and transform numerical features according to model requirements and data skew.

Select a useful feature subset using validation performance rather than test performance.

Apply principal component analysis and quantify retained variance.

Cluster standardized observations with k-means and assess cluster stability.

Detect unusual observations with an isolation-based anomaly detector.

Use slice-based error analysis to find weak performance on meaningful data subgroups.

Estimate global feature importance with permutation-based analysis.

Create PyTorch tensors and move computations explicitly between CPU and accelerator devices.

Use automatic differentiation to compute gradients for a scalar loss.

Define a neural network with reusable PyTorch modules and activation functions.

Load mini-batches with Dataset and DataLoader abstractions.

Implement a complete PyTorch training and validation loop.

Apply initialization, normalization, dropout, and weight decay to stabilize training.

Diagnose tensor-shape, gradient, and device-placement failures in a training run.

Save and restore model weights together with the preprocessing state needed for inference.

Train a multilayer perceptron for a tabular prediction task.

Train a convolutional neural network for a small image-classification task.

Apply image augmentation and measure its effect on generalization.

Train a text classifier using token embeddings and pooled sequence representations.

Compute scaled dot-product self-attention for a small sequence.

Fine-tune a pretrained model while controlling which parameters are updated.

Perform modality-appropriate error analysis on image or text predictions.

Compare a deep model with a simpler baseline using accuracy, latency, and data requirements.

Organize data, training, evaluation, and inference code as a testable Python package.

Manage experiment settings with validated configuration files and command-line parameters.

Write unit tests for feature transformations and model input-output contracts.

Version dataset definitions and record lineage from raw data to model artifact.

Track training runs, metrics, parameters, and artifacts with an experiment tracker.

Promote and retrieve versioned model artifacts through a model registry.

Construct a repeatable training pipeline from preprocessing through evaluation.

Reproduce a selected experiment in a clean environment from recorded code, data, and configuration versions.

Package preprocessing and prediction behind a single versioned inference interface.

Implement an HTTP prediction endpoint with FastAPI.

Validate inference requests and return actionable errors for malformed inputs.

Choose between batch and online inference from latency, volume, and freshness requirements.

Containerize an inference service with a minimal reproducible Docker image.

Deploy a containerized prediction service to a managed cloud runtime.

Load-test an inference endpoint and report latency percentiles, throughput, and failures.

Add timeouts, health checks, structured logs, and safe failure behavior to an inference service.

Create a continuous-integration workflow that tests ML code and container builds.

Design a versioned release process for models and inference services.

Monitor request rate, errors, resource use, and latency for a deployed model service.

Detect schema changes and distribution drift in production inputs.

Measure prediction drift and delayed-label model performance separately.

Define evidence-based triggers for model investigation or retraining.

Compare shadow, canary, and rollback strategies for releasing a new model version.

Diagnose a simulated production incident using service, data, and model signals.

Distinguish latency, throughput, utilization, memory footprint, and cost as inference objectives.

Design a reproducible benchmark with warm-up runs, fixed inputs, and latency percentiles.

Profile a CPU inference workload to identify expensive operators and preprocessing bottlenecks.

Explain how GPU kernels, memory transfers, synchronization, and launch overhead affect inference.

Tune batch size and concurrency against latency and throughput constraints.

Compare FP32, FP16, BF16, and integer formats in terms of range, precision, memory, and hardware support.

Export a supported PyTorch model to ONNX with explicit input and output specifications.

Validate numerical correctness and benchmark performance across two inference runtimes.

Use profiling evidence to classify an inference workload as compute-, memory-, transfer-, or overhead-bound.

Apply mixed-precision inference and verify its accuracy and performance effects.

Apply post-training quantization and measure model size, latency, and quality changes.

Use graph compilation to reduce framework and operator-dispatch overhead.

Explain how operator fusion and memory layout can reduce data movement.

Evaluate pruning or knowledge distillation as a model-level optimization strategy.

Select an inference runtime and hardware target from model operators, service constraints, and cost.

Define service-level objectives and produce an evidence-based optimization plan for an end-to-end ML system.