ML Architect

ML Theory & Foundations

Why: You can reason about generalization when choosing model capacity for a new problem, instead of relying on trial and error against the test set.

Statistical Foundations

Why: You can apply Bayesian or maximum likelihood reasoning to quantify uncertainty in a prediction, which matters when a stakeholder asks how sure the model is.

Neural Network Fundamentals

Why: You can debug a network that will not train by reasoning about activations, backpropagation and the optimizer, instead of changing settings at random.

Deep Learning Architectures

Why: You can pick between a CNN, an LSTM or an attention-based model for a given data type, matching the architecture to the structure of the problem.

Feature Engineering & Selection

Why: You can build and select features with a clear view of which ones will be available at serving time, avoiding features that exist only in historical data.

Data Preparation & Quality

Why: You can set up data validation checks that run before every training, so a quality regression in a source table fails a check instead of a model.

Model Serving & APIs

Why: You can expose a model as an API or inference service with defined latency and input contracts, so another team can integrate without reading your code.

Model Monitoring & Observability

Why: You can monitor prediction quality, data drift and service health after launch, so you hear about a degrading model from a dashboard and not from users.

Fairness & Bias Mitigation

Why: You can compute fairness metrics across groups and flag a disparity during review, before a model that treats groups differently is released.

Explainability & Interpretability

Why: You can show which features drove a prediction and explain it in plain language to a non-technical reviewer who must approve the model.