papers/hanzo-ai-commerce/sections/04-implementation.tex

\section{Implementation}


\subsection{Technology Stack}

\begin{itemize}
\item \textbf{Event streaming}: Apache Kafka.
\item \textbf{Feature store}: Custom implementation on Redis (online) + BigQuery (offline).
\item \textbf{Model training}: TensorFlow, XGBoost.
\item \textbf{Model serving}: TensorFlow Serving, custom Python services.
\item \textbf{Orchestration}: Kubernetes, Airflow.
\end{itemize}

\subsection{Latency Requirements}

Commerce AI must meet strict latency requirements:

\begin{table}[h]
\centering
\caption{Latency requirements by surface}
\label{tab:latency}
\begin{tabular}{lrr}
\hline
Surface & p50 (ms) & p99 (ms) \\
\hline
Search ranking & 20 & 50 \\
Recommendations & 15 & 40 \\
Fraud scoring & 100 & 250 \\
Dynamic pricing & 50 & 150 \\
\hline
\end{tabular}
\end{table}

We achieve these through:
\begin{itemize}
\item Feature caching with 1-minute TTL.
\item Batch prediction for non-real-time surfaces.
\item Model quantization and optimization.
\item Geographic distribution (models deployed to edge).
\end{itemize}

\subsection{Model Updates}

Models update through a continuous training pipeline:

\begin{enumerate}
\item New events flow to training data store.
\item Nightly jobs retrain models on recent data.
\item Shadow deployment compares new model to production.
\item Automatic promotion if metrics improve.
\item Rollback if degradation detected.
\end{enumerate}

\begin{lstlisting}[language=Python,caption=Model promotion logic]
def should_promote(shadow_metrics, prod_metrics):
    # Require improvement on primary metric
    if shadow_metrics.conversion_rate < prod_metrics.conversion_rate:
        return False

    # Guard rails on secondary metrics
    if shadow_metrics.latency_p99 > prod_metrics.latency_p99 * 1.1:
        return False

    # Statistical significance
    if not is_significant(shadow_metrics, prod_metrics, alpha=0.05):
        return False

    return True
\end{lstlisting}