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.

Choose and fit numerical transformations and categorical encoders using training data only.

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.

Diagnose underfitting and overfitting with validation results and learning curves.

Apply L1 and L2 regularization and compare their effects on validation performance.

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.

Assess and improve probability estimates using calibration curves and a proper scoring rule.

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.

Apply class weighting or resampling to an imbalanced classification problem.

Engineer task-informed features inside a leakage-safe pipeline and measure their effect on validation performance.

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

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

Estimate uncertainty in an evaluation metric using a bootstrap confidence interval.

Apply principal component analysis and quantify retained variance.

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

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.

Select and apply normalization, dropout, or weight decay in response to a diagnosed training or generalization problem.

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 predictive quality, 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.

Construct a repeatable training pipeline from preprocessing through evaluation.

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

Promote and retrieve versioned model artifacts through a model registry.

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.

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

Implement an HTTP prediction endpoint with FastAPI.

Validate inference requests and return actionable errors for malformed inputs.

Containerize an inference service with a minimal reproducible Docker image.

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

Deploy a containerized prediction service to a managed cloud runtime.

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

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.

Translate latency, throughput, utilization, memory, and cost requirements into measurable 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.

Interpret a GPU profiler trace to identify kernel execution, memory transfers, synchronization, and launch overhead.

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.

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

Apply mixed-precision inference and verify its predictive-quality and performance effects.

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

Compile a model graph, identify graph breaks or unsupported operators, and measure the effect of supported fusion.

Apply structured pruning, rebuild the compact model, and verify whether size reduction produces real runtime gains.

Implement teacher-student knowledge distillation to train a compact model while preserving performance.

Produce an end-to-end optimization report that documents service objectives, benchmark conditions, correctness tolerances, measured gains, and prioritized next steps.