Show HN: Endgame – Production-aware ML under the sklearn API
by cameronhamilton | 2026-03-03 22:27:20+00:00
Most ML frameworks optimize for leaderboard accuracy.
But in finance and healthcare, accuracy is often the least interesting part of the system.
If you can’t explain a prediction, you can’t deploy it.
If your probabilities aren’t calibrated, you can’t trust them.
If your pipeline doesn’t enforce constraints, you can’t ship it.
I built Endgame after repeatedly running into that gap in production.

Anti-money laundering (banks)
Early in my career, I was hired to improve an anti-money laundering system.
The incumbent model was 28 hard-coded rules.
If enough thresholds fired (e.g., $3,000 ATM withdrawals over 30 days), the account was flagged.
No one knew where the thresholds came from.
There was no modeling of the underlying behavior.
Just rule accumulation.
I convinced the bank to provide the raw financial features behind those rule firings.
We trained an interpretable ML model directly on the underlying activity patterns.
The result: ~200% more true positives (accounts actually involved in fraud or laundering).
But what leadership cared about most wasn’t the metric.
It was this:
“Why is this account suspicious?”
That theme repeated across industries.

Insurance claim adjudication
I later built a claim adjudication model for a major health insurer.
The legacy system was massive, brittle, and effectively a black box.
It would frequently deny claims incorrectly, and no one fully understood how it worked.
We built a new ML system that brought claim-level adjudication accuracy to ~95%.
Again, the metric wasn’t the headline internally.
The headline was:
“Why did this claim get denied?”
In regulated environments, interpretability isn’t optional.

Stock forecasting and calibration
I also learned this lesson personally.
I built stock-forecasting models that performed well in historical backtests.
Some predictions showed 80% probability of a price increase.
Then the market regime shifted.
The probabilities were overconfident.
Some trades went the opposite direction.
I lost money.
Accuracy ≠ trustworthy probabilities.
Calibration and drift awareness matter far more in deployment than most tutorials suggest.
That experience fundamentally changed how I think about ML systems.

The core idea
Endgame is my attempt to encode those lessons into a framework.
It’s not trying to replace scikit-learn.
Every estimator implements fit / predict / transform.
But it extends the ecosystem with:
Glass-box models (EBM, GAM, CORELS, SLIM, GOSDT, etc.)
SOTA deep tabular models (FT-Transformer, TabPFN, SAINT, etc.)
Conformal prediction and Venn-ABERS calibration
Deployment guardrails (leakage detection, latency constraints, drift checks)
42 self-contained HTML visualizations
Super Learner, BMA, cascade ensembles
A full AutoML pipeline that respects deployment constraints
All under a unified sklearn-compatible API.

Agent-native ML (MCP)
We’re in the agentic AI era.
You can ask an LLM to build a pipeline for you, but it often requires multiple prompts and manual corrections.
Endgame ships with a native MCP server.
This lets agents:
load data
train models
compare results
generate reports
export reproducible scripts
Through structured tool calls, not fragile prompt chains.
My belief is that ML pipelines will increasingly become conversational infrastructure.

A small contrarian view
The ML community is underestimating the problems left to solve in tabular data
and overestimating the demand for accuracy-optimized models.
Most real-world data in business, healthcare, and finance is tabular (often multimodal).
And most real-world systems need to be interpretable, calibrated, and deployable — not just accurate.

Endgame v1.0.0 is open source (Apache 2.0).
Python 3.10+.
If you work on production ML systems, especially in regulated domains, I’d genuinely value feedback.
GitHub: https://github.com/allianceai/endgame
Install: pip install endgame-ml
Happy to answer technical questions.