Your models should tune themselves.
We plug into data teams drowning in hyperparameter tuning and pipeline plumbing — and hand back production-ready AutoML workflows that cut experiment cycles from weeks to hours.
Your team is maintaining infrastructure, not building models.
Manual Grid Search
Your team is running 400-trial exhaustive sweeps on a single machine. The experiment queue is 3 weeks long. Two engineers quit.
Pipeline Drift
The model that scored 94% in staging scores 71% in production. Nobody knows why. The feature store and the training script diverged 6 months ago.
Shadow IT Models
Three notebooks, a cron job, and a Slack bot are your AI roadmap. One senior engineer is the single point of failure.
Production-ready AutoML, handed back in days.
Bayesian Optimization
Optuna-powered search with pruning. We find better hyperparameters in 40 trials than your team finds in 4,000. Every search is logged, reproducible, and version-controlled.
Automated Feature Stores
Feast-backed feature pipelines with point-in-time correctness. Training and serving share the same feature definitions. Pipeline drift becomes structurally impossible.
ML CI/CD Pipelines
Every model change triggers automated retraining, evaluation, shadow deployment, and canary rollout. Your notebook becomes a production system in one sprint.
Explore the benchmark data
Sort by latency, accuracy, or cost. All numbers from real production workloads.
| approach | P95 Latency (ms) | Val Accuracy (%)↓ | Cost / 1k inf ($) | Setup Days |
|---|---|---|---|---|
Autofit Bayesian Pipelineautofit | 18 | 96.4 | $0.42 | 1d |
Optuna (manual setup) | 29 | 95.1 | $0.85 | 8d |
Vertex AI AutoML | 38 | 94.1 | $2.1 | 5d |
AutoML (H2O AutoML) | 45 | 93.8 | $1.2 | 3d |
SageMaker Autopilot | 52 | 92.7 | $3.4 | 7d |
Manual Grid Search (SKLearn) | 312 | 91.2 | $4.8 | 14d |
Random Search + MLflow | 187 | 89.4 | $2.9 | 12d |
These numbers are from real client workloads. Yours will vary — but not by much.
ML teams who stopped tuning by hand
"We were running 600-trial grid searches on a 32-core box and still missing our weekly model refresh SLA. Autofit moved us to Bayesian search in 4 days. We now run 50 trials and hit better val accuracy. The Databricks bill dropped $8,400/month."
"I was the single point of failure for our entire ML stack. Three notebooks and a cron job. Autofit delivered a proper CI/CD pipeline with shadow deployment in one sprint. I slept through a Saturday for the first time in 14 months."
"Our CTO asked why the AI roadmap was three notebooks. I didn't have a good answer. Autofit audited our pipeline on a Tuesday, delivered a feature store design on Thursday, and we had our first automated retraining job in production by the following Friday."
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