Learn the mechanism. Keep the evidence.
InterviewsVector’s original AI Engineering Academy connects derivation, code, failure analysis, production constraints, and interview defense.
Open the capability roadmap- Arcs
- 11
- Labs live
- 38
- Mapped
- 76
Published original labs
01Draw the AI System BoundarySeparate deterministic software, probabilistic model behavior, data, and human judgment before choosing tools.55 min →02Reproducible Experiments, Not Reproducible NotebooksMake code, data, seeds, dependencies, and hardware assumptions inspectable.80 min →03Data Contracts for ModelsSpecify schema, meaning, time, lineage, and allowed use before training or retrieval.70 min →04Define the Measurement Before the ModelTranslate a product claim into offline, online, safety, latency, and cost measures.65 min →05A First AI Workload Cost ModelEstimate tokens, accelerator time, storage, and network movement before committing architecture.75 min →06From Experiment to Service BoundaryExtract a tested model contract from exploratory code without copying notebook state into production.75 min →07Vectors as RepresentationsConnect coordinates, basis, scale, and information loss to learned representations.80 min →08Similarity Is a Retrieval PolicyDerive dot product and cosine similarity, then show why normalization changes what a retrieval system rewards.95 min →09Matrices as Small ProgramsRead a matrix as a composition of projection, scaling, rotation, and mixing operations.90 min →010Probability for Decisions, Not DecorationUse conditional probability, calibration, and expected cost to make model outputs actionable.95 min →011Optimization Under NoiseDerive stochastic gradients and distinguish noisy progress from broken learning.100 min →012Numerical Stability Is Part of the AlgorithmBreak softmax, log likelihood, and low-precision reductions, then repair them.100 min →013Complexity for Tensor ProgramsEstimate FLOPs, memory traffic, activation storage, and asymptotic traps from tensor shapes.85 min →014The Generalization ContractDefine the population, decision, loss, and evidence under which performance is expected to transfer.90 min →015Split Data by Causality, Not ConvenienceUse information availability, time, entity, group, and provenance to construct honest evaluation partitions.105 min →016Linear Models as Debugging InstrumentsUse transparent linear baselines to expose target, feature, split, scale, and slice failures before adding complexity.110 min →017Trees, Boosting, and the Shape of Residual ErrorUnderstand how tree ensembles partition mistakes and when their inductive bias wins.110 min →018Calibration, Thresholds, and Decision CostTurn scores into actions using reliability curves and asymmetric error costs.100 min →019When the Product Needs Ranking, Not ClassificationChoose ranking losses and metrics when ordering quality matters more than labels.90 min →020Drift, Feedback Loops, and Delayed LabelsDesign monitoring when the system changes the data it later learns from.110 min →021A Neuron Is a Parameterized Decision SurfaceConnect affine maps and nonlinearities to representation capacity.80 min →022Computation Graphs Make Learning InspectableRepresent forward values, dependencies, and local derivatives explicitly.90 min →023Backpropagation as Local ContractsDerive reverse-mode autodiff as small, testable vector-Jacobian products rather than one mysterious global formula.120 min →024Read Optimization DynamicsUse loss curves, gradient statistics, and parameter updates to separate data, optimization, and capacity failures.105 min →025Normalization and Residual PathsUnderstand why signal and gradient paths shape trainability in deep networks.105 min →026Convolutions and Locality as an Inductive BiasBuild a convolution and connect weight sharing to images, audio, and structured grids.105 min →027Neural Training Incident: Loss Becomes NaNTrace a realistic failure through inputs, precision, activations, gradients, and optimizer state.110 min →028Tokenization as a Compression ContractStudy how vocabulary construction changes sequence length, multilingual behavior, cost, and failure modes.105 min →029Derive Attention from Content-Based RoutingBuild scaled dot-product attention from the need to route information between positions.120 min →030Multi-Head Attention Is Parallel Representation RoutingExplain head dimension, projections, and what head diversity does and does not guarantee.95 min →031Position, Context, and ExtrapolationCompare positional mechanisms by the invariants they encode and how they fail outside training lengths.105 min →032Assemble and Test a Transformer BlockCompose attention, MLP, normalization, masking, and residual paths with shape tests.140 min →033Pretraining Objectives Shape Model BehaviorConnect next-token prediction, masking, data mixtures, and preference objectives to observable capabilities.105 min →034Decoding Is a Product PolicyTreat temperature, top-p, constraints, and stopping as explicit quality and risk decisions.90 min →035The KV Cache Capacity PlanDerive per-token cache memory, then connect context length, concurrency, precision, batching, and paging to serving capacity.120 min →036Diagnose RAG by StageMeasure corpus, retrieval, reranking, context, generation, citation, latency, and cost separately so failures remain attributable.125 min →037A Tool-Using Agent Is a Bounded State MachineImplement budgets, typed transitions, approval gates, idempotency, and terminal states around a probabilistic planner.135 min →038Design a Multi-Tenant AI GatewayCreate one policy and observability boundary for provider routing, tenant isolation, budgets, fallback, and release evidence.145 min →