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Engineering foundations
A churn notebook cannot be reproduced by the teammate expected to ship it. A seeded, tested, packaged, versioned training workflow with a model card.
role:platform · lifecycle:training · concept:reproducibilityCourseProblem framing, statistics & evaluation
An ETA model improves RMSE while increasing missed-delivery cost. A metric contract, leakage-safe validation, calibration analysis, and experiment plan.
lifecycle:evaluation · concept:calibration · concept:uncertaintyCourseData & feature foundations
Fraud labels arrive late and online features disagree with training data. Data contracts, point-in-time features, backfills, quality gates, and lineage.
lifecycle:data · failure:training-serving-skew · role:platformCourseLinear, probabilistic, neighbor & margin models
Support tickets need explainable routing under a 15 ms latency budget. A justified baseline across regression, Naive Bayes, kNN, and SVM.
algorithm:knn · algorithm:svm · modality:tabularCourseTrees, forests & gradient boosting
A marketplace must catch rare fraud without blocking trustworthy sellers. Calibrated random-forest and XGBoost baselines with cost-sensitive thresholds.
algorithm:random-forest · algorithm:xgboost · concept:calibrationCourseUnsupervised learning, anomalies & retrieval
Novel equipment failures appear before reliable labels exist. A PCA, clustering, anomaly, and nearest-neighbor retrieval investigation.
algorithm:pca · concept:embeddings · lifecycle:evaluationCourseForecasting & temporal ML
Regional demand shifts through promotions, holidays, and drift. Rolling validation, statistical baselines, boosted forecasts, and uncertainty bands.
modality:time-series · concept:uncertainty · failure:driftCourseRecommendation, search & ranking
Marketplace ranking learns from biased clicks and reinforces its own mistakes. Candidate generation, ranking metrics, debiasing, and online experiment design.
track:recommendation-search · failure:feedback-loop · concept:embeddingsCourseNeural networks from scratch
A nonlinear sensor classifier defeats every classical baseline. A small network built from forward pass through backpropagation and optimization.
concept:backpropagation · lifecycle:training · tool:pytorchCourseImage ML
A factory has few positive examples of costly visual defects. CNN and transfer-learning baselines with augmentation, calibration, and monitoring.
modality:image · algorithm:cnn · track:vision-multimodalCourseSequence ML: RNNs, GRUs & LSTMs
Long support conversations must be routed while preserving temporal context. A masked sequence model with BPTT, recurrent gates, and attention precursors.
modality:text · algorithm:lstm · track:nlp-llmCourseGPU-aware training
Training repeatedly OOMs and misses its nightly completion window. A profiled, mixed-precision, memory-budgeted training loop.
tool:cuda · failure:oom · lifecycle:trainingCourseDistributed & fault-tolerant training
An eight-GPU job is slower than one GPU and loses progress on failure. Efficient DDP, parallelism choices, elastic sampling, and resumable checkpoints.
concept:all-reduce · tool:pytorch · role:platformCourseBuild a transformer from scratch
Domain autocomplete needs a model whose every component the team can explain. Tokenizer, attention, RoPE, residual stack, training loop, generation, and KV cache.
concept:attention · algorithm:transformer · track:nlp-llmCourseFoundation-model lifecycle
A private support assistant must answer from evidence and resist manipulation. A grounded system design spanning adaptation, RAG, evaluation, and safety.
concept:rag · concept:evaluation · failure:prompt-injectionCourseFrontier architectures & efficient inference
Long-context generation breaks latency and cost targets. A capacity plan using efficient attention, quantization, batching, and modern routing.
concept:kv-cache · tool:vllm · lifecycle:inferenceCourseProduction data & ML platforms
A late feature silently corrupts every downstream prediction. A traced batch/stream platform with orchestration, registry, lineage, and ownership.
tool:kafka · tool:mlflow · role:platformCourseServing & inference systems
Launch traffic causes p99 spikes and leaves expensive GPUs idle. Batch and online paths with batching, autoscaling, load tests, canaries, and rollback.
lifecycle:inference · tool:triton · failure:latencyCourseReliability, monitoring & experimentation
Model quality decays while every infrastructure dashboard stays green. Joined data/model/service telemetry, drift policy, experiments, alerts, and runbooks.
failure:drift · lifecycle:monitoring · role:reliabilityCourseGovernance, fairness, privacy & security
A high-performing system creates subgroup harm and leaks sensitive information. A launch review covering fairness, privacy, licensing, abuse, security, and response.
concept:fairness · concept:privacy · failure:supply-chainLessonFrom notebook to reproducible run
Use from notebook to reproducible run to move the engineering foundations production brief toward a defensible release.
role:platform · lifecycle:training · concept:reproducibilityLessonContracts before models
Use contracts before models to move the engineering foundations production brief toward a defensible release.
role:platform · lifecycle:training · concept:reproducibilityLessonTraining code is product code
Use training code is product code to move the engineering foundations production brief toward a defensible release.
role:platform · lifecycle:training · concept:reproducibilityLessonContainers and configuration without surprises
Use containers and configuration without surprises to move the engineering foundations production brief toward a defensible release.
role:platform · lifecycle:training · concept:reproducibilityLessonObservable, idempotent, failure-safe workflows
Use observable, idempotent, failure-safe workflows to move the engineering foundations production brief toward a defensible release.
role:platform · lifecycle:training · concept:reproducibilityLessonRead a research paper as an executable specification
Turn a research paper into a testable claim map, implementation contract, and prioritized reading plan before writing code.
role:platform · lifecycle:training · concept:reproducibilityLessonReproduce, validate, and productionize a paper
Implement a paper from baseline to novel component, diagnose reproduction gaps, and adapt it for production without silently changing its claim.
role:platform · lifecycle:training · concept:reproducibilityLessonPredict decisions, not just labels
Use predict decisions, not just labels to move the evaluation production brief toward a defensible release.
lifecycle:evaluation · concept:calibration · concept:uncertaintyLessonLabels, availability, and leakage
Use labels, availability, and leakage to move the evaluation production brief toward a defensible release.
lifecycle:evaluation · concept:calibration · concept:uncertaintyLessonBaselines and honest splits
Use baselines and honest splits to move the evaluation production brief toward a defensible release.
lifecycle:evaluation · concept:calibration · concept:uncertaintyLessonMetrics, thresholds, and calibration
Use metrics, thresholds, and calibration to move the evaluation production brief toward a defensible release.
lifecycle:evaluation · concept:calibration · concept:uncertaintyLessonUncertainty and online experiments
Use uncertainty and online experiments to move the evaluation production brief toward a defensible release.
lifecycle:evaluation · concept:calibration · concept:uncertaintyLessonMetric selection by task and decision
Select metrics from the prediction target, decision, error costs, prevalence, slices, horizon, and deployment constraints—not from habit.
lifecycle:evaluation · concept:calibration · concept:uncertaintyLessonData contracts, ownership, and lineage
Use data contracts, ownership, and lineage to move the data & features production brief toward a defensible release.
lifecycle:data · failure:training-serving-skew · role:platformLessonPoint-in-time-correct features
Use point-in-time-correct features to move the data & features production brief toward a defensible release.
lifecycle:data · failure:training-serving-skew · role:platformLessonFeature transformations that survive production
Use feature transformations that survive production to move the data & features production brief toward a defensible release.
lifecycle:data · failure:training-serving-skew · role:platformLessonBatch, stream, and training-serving parity
Use batch, stream, and training-serving parity to move the data & features production brief toward a defensible release.
lifecycle:data · failure:training-serving-skew · role:platformLessonLabels, snapshots, and backfills
Use labels, snapshots, and backfills to move the data & features production brief toward a defensible release.
lifecycle:data · failure:training-serving-skew · role:platformLessonLinear regression as a production baseline
Use linear regression as a production baseline to move the classical baselines production brief toward a defensible release.
algorithm:knn · algorithm:svm · modality:tabularLessonLogistic regression, odds, and calibration
Use logistic regression, odds, and calibration to move the classical baselines production brief toward a defensible release.
algorithm:knn · algorithm:svm · modality:tabularLessonNaive Bayes under sparse data
Use naive bayes under sparse data to move the classical baselines production brief toward a defensible release.
algorithm:knn · algorithm:svm · modality:tabularLessonkNN, distance, and local decisions
Use knn, distance, and local decisions to move the classical baselines production brief toward a defensible release.
algorithm:knn · algorithm:svm · modality:tabularLessonSVMs, margins, and kernels
Use svms, margins, and kernels to move the classical baselines production brief toward a defensible release.
algorithm:knn · algorithm:svm · modality:tabularLessonTrees as learned decision rules
Use trees as learned decision rules to move the trees & boosting production brief toward a defensible release.
algorithm:random-forest · algorithm:xgboost · concept:calibrationLessonRandom forests and diversity
Use random forests and diversity to move the trees & boosting production brief toward a defensible release.
algorithm:random-forest · algorithm:xgboost · concept:calibrationLessonGradient boosting as error correction
Use gradient boosting as error correction to move the trees & boosting production brief toward a defensible release.
algorithm:random-forest · algorithm:xgboost · concept:calibrationLessonXGBoost: regularized boosting at scale
Use xgboost: regularized boosting at scale to move the trees & boosting production brief toward a defensible release.
algorithm:random-forest · algorithm:xgboost · concept:calibrationLessonCalibrating and explaining ensembles
Use calibrating and explaining ensembles to move the trees & boosting production brief toward a defensible release.
algorithm:random-forest · algorithm:xgboost · concept:calibrationLessonPCA and representation geometry
Use pca and representation geometry to move the unsupervised & retrieval production brief toward a defensible release.
algorithm:pca · concept:embeddings · lifecycle:evaluationLessonClustering as an operational hypothesis
Use clustering as an operational hypothesis to move the unsupervised & retrieval production brief toward a defensible release.
algorithm:pca · concept:embeddings · lifecycle:evaluationLessonDensity clusters and novel anomalies
Use density clusters and novel anomalies to move the unsupervised & retrieval production brief toward a defensible release.
algorithm:pca · concept:embeddings · lifecycle:evaluationLessonEmbeddings and similarity
Use embeddings and similarity to move the unsupervised & retrieval production brief toward a defensible release.
algorithm:pca · concept:embeddings · lifecycle:evaluationLessonApproximate retrieval under latency
Use approximate retrieval under latency to move the unsupervised & retrieval production brief toward a defensible release.
algorithm:pca · concept:embeddings · lifecycle:evaluationLessonForecast contracts and naive models
Use forecast contracts and naive models to move the forecasting production brief toward a defensible release.
modality:time-series · concept:uncertainty · failure:driftLessonTrend, seasonality, and known-future signals
Use trend, seasonality, and known-future signals to move the forecasting production brief toward a defensible release.
modality:time-series · concept:uncertainty · failure:driftLessonBacktesting without time travel
Use backtesting without time travel to move the forecasting production brief toward a defensible release.
modality:time-series · concept:uncertainty · failure:driftLessonStatistical and ML forecasters
Use statistical and ml forecasters to move the forecasting production brief toward a defensible release.
modality:time-series · concept:uncertainty · failure:driftLessonIntervals, drift, and forecast operations
Use intervals, drift, and forecast operations to move the forecasting production brief toward a defensible release.
modality:time-series · concept:uncertainty · failure:driftLessonFrom corpus to slate: retrieve, score, re-rank
Use from corpus to slate: retrieve, score, re-rank to move the ranking systems production brief toward a defensible release.
track:recommendation-search · failure:feedback-loop · concept:embeddingsLessonClicks are not labels
Use clicks are not labels to move the ranking systems production brief toward a defensible release.
track:recommendation-search · failure:feedback-loop · concept:embeddingsLessonBM25, factorization, two towers, and ANN
Use bm25, factorization, two towers, and ann to move the ranking systems production brief toward a defensible release.
track:recommendation-search · failure:feedback-loop · concept:embeddingsLessonPointwise, pairwise, and listwise ranking
Use pointwise, pairwise, and listwise ranking to move the ranking systems production brief toward a defensible release.
track:recommendation-search · failure:feedback-loop · concept:embeddingsLessonServing freshness, diversity, and experiments
Use serving freshness, diversity, and experiments to move the ranking systems production brief toward a defensible release.
track:recommendation-search · failure:feedback-loop · concept:embeddingsLessonBeyond linear boundaries
Use beyond linear boundaries to move the neural networks production brief toward a defensible release.
concept:backpropagation · lifecycle:training · tool:pytorchLessonForward pass, activations, losses, and initialization
Use forward pass, activations, losses, and initialization to move the neural networks production brief toward a defensible release.
concept:backpropagation · lifecycle:training · tool:pytorchLessonBackpropagation without magic
Use backpropagation without magic to move the neural networks production brief toward a defensible release.
concept:backpropagation · lifecycle:training · tool:pytorchLessonSGD, momentum, AdamW, and schedules
Use sgd, momentum, adamw, and schedules to move the neural networks production brief toward a defensible release.
concept:backpropagation · lifecycle:training · tool:pytorchLessonRegularize, normalize, debug, and ship
Use regularize, normalize, debug, and ship to move the neural networks production brief toward a defensible release.
concept:backpropagation · lifecycle:training · tool:pytorchLessonActivation functions: geometry, gradients, and failure modes
Select activation functions by signal range, gradient flow, architecture, precision, and failure mode; then diagnose saturation and dead units from telemetry.
concept:backpropagation · lifecycle:training · tool:pytorchLessonLoss functions by task, data, and deployment constraints
Choose and debug training objectives as optimization surrogates for the real task, including imbalance, outliers, uncertainty, ranking, dense prediction, and generation.
concept:backpropagation · lifecycle:training · tool:pytorchLessonImages are data contracts
Use images are data contracts to move the vision production brief toward a defensible release.
modality:image · algorithm:cnn · track:vision-multimodalLessonConvolution under the microscope
Use convolution under the microscope to move the vision production brief toward a defensible release.
modality:image · algorithm:cnn · track:vision-multimodalLessonReceptive fields, normalization, and residuals
Use receptive fields, normalization, and residuals to move the vision production brief toward a defensible release.
modality:image · algorithm:cnn · track:vision-multimodalLessonTransfer learning for classification, detection, and segmentation
Use transfer learning for classification, detection, and segmentation to move the vision production brief toward a defensible release.
modality:image · algorithm:cnn · track:vision-multimodalLessonVision in the wild: thresholds, latency, and drift
Use vision in the wild: thresholds, latency, and drift to move the vision production brief toward a defensible release.
modality:image · algorithm:cnn · track:vision-multimodalLessonSequences are not IID
Use sequences are not iid to move the sequence models production brief toward a defensible release.
modality:text · algorithm:lstm · track:nlp-llmLessonRecurrent state and BPTT
Use recurrent state and bptt to move the sequence models production brief toward a defensible release.
modality:text · algorithm:lstm · track:nlp-llmLessonGates that remember: LSTM and GRU
Use gates that remember: lstm and gru to move the sequence models production brief toward a defensible release.
modality:text · algorithm:lstm · track:nlp-llmLessonMany-to-one and many-to-many training
Use many-to-one and many-to-many training to move the sequence models production brief toward a defensible release.
modality:text · algorithm:lstm · track:nlp-llmLessonStateful streaming inference without future leakage
Use stateful streaming inference without future leakage to move the sequence models production brief toward a defensible release.
modality:text · algorithm:lstm · track:nlp-llm