We don’t wrap APIs and call it AI. Our ML engineer holds a master’s in machine learning, designs training pipelines from scratch, and deploys models that retrain automatically. Live proof: mmamodel.ai predicts UFC fight outcomes with a 5-model stacked ensemble, weekly automated retraining, and a live FastAPI serving layer.
The agency market is full of projects that bolt GPT-4 onto a database and call it AI. That’s a useful tool for text tasks. It’s not machine learning engineering. Here’s what separates the two.
The temporal validation point is critical. Most ML projects built by generalist developers suffer from data leakage — they train on future data and report inflated accuracy numbers that collapse in production. Real ML engineering uses purged walk-forward cross-validation: the model is only ever evaluated on data it could not have seen at training time. This is the difference between genuine 67.6% holdout accuracy and 67.6% wishful thinking.
Production ML systems, not prototypes. Every system is deployed, tested under real conditions, and monitored in production.
Binary and multi-class classifiers that output calibrated probabilities — not just a label. Winner prediction, outcome classification, churn prediction, demand forecasting. We calibrate models so that when we say “68% probability,” it actually means 68% — verified against historical holdout data your model never saw during training.
Scoring pipelines that run at massive scale inside your database. Our GovGreed platform scores 13,052 active politician × bill predictions using PostgreSQL stored functions — no external Python runtime, no API latency, zero per-prediction compute cost. Scores refresh automatically as underlying data changes via database triggers.
Multiple models trained independently, combined through a meta-learner trained on out-of-fold predictions. Stacking reliably outperforms any single model by exploiting different inductive biases across algorithms. LightGBM, XGBoost, CatBoost, neural networks, and logistic regression working together, with the meta-learner learning which model to trust in which situations.
Models that improve as your data grows. GitHub Actions cron triggers the full pipeline (data pull → feature engineering → training → walk-forward validation → deployment) on your chosen schedule. The new model is only promoted if it clears a minimum accuracy threshold — if retraining produces a worse model, the previous version stays live automatically. Zero human intervention required.
This is not a demo or white-paper. mmamodel.ai is a live production system serving predictions to real users every event week, retraining every Monday morning, and logging every prediction to Supabase for full auditability. The engineering decisions below are real and verifiable.
UFC fight prediction is a canonically hard ML problem. The training data is strictly temporal — fight outcomes happen in sequence — which means naive cross-validation leaks future fight statistics into training, producing models that look accurate in development and fail in production.
Fighter statistics accumulate over time. If you compute a fighter’s win streak using their full career record and then train on historical fights, you’re using information that didn’t exist at fight time. Most sports prediction projects get this wrong. Every feature in our pipeline is computed using only data that existed on the date of each training fight — enforced by a 90-day temporal buffer.
Purged walk-forward cross-validation with a 90-day temporal buffer between training and test sets. The purge gap prevents fighter statistics from adjacent time boundaries contaminating training features. All folds are strictly chronological — no random shuffling ever. This is the only methodologically correct approach for sports prediction models.
200 Optuna trials per base model using Tree-structured Parzen Estimators (TPE). Bayesian optimization reliably finds better hyperparameters than grid search using roughly 10x fewer compute trials. Each trial runs the complete walk-forward CV pipeline — no shortcuts, no approximations in the validation loop.
4-dimensional Elo/Glicko-2 ratings tracking striking, grappling, wrestling, and overall performance separately. Dual-track EWMA stats with short and long decay constants to capture recent form vs. career baseline. Bayesian shrinkage on small-sample fighters prevents overfitting debut records. Stance-normalized differentials across 40+ per-fight attributes.
The GovGreed corruption prediction platform scores U.S. politicians against pending bills — 13,052 active ML predictions — all stored directly inside PostgreSQL as computed values alongside the source data.
There are no external model files to manage. No Python runtime that needs to stay running. The scoring logic lives in PostgreSQL stored functions triggered by data changes. When a new bill is inserted, scores compute automatically. When a legislator’s stock trades update, affected predictions refresh without any manual pipeline run.
This is what database-native ML looks like in practice: zero infrastructure overhead, zero API round-trip latency, zero per-prediction compute cost after the initial model fit. The entire prediction layer survives a server restart because it’s in the database.
Every library chosen because it’s the best tool for the job — not the most popular. We switch when better options exist.
Answered directly. No sales fluff.
Tell us your prediction problem. We will tell you whether ML is the right tool, what data you need, what accuracy to expect, and what it will cost. No sales theater — a direct technical conversation with the engineer who will build it.