Data validation
Ana checks schemas, distributions and freshness before anything trains, and flags label leakage and train/serve skew up front.
Schema checksLeakage detectionTrain/serve skew
Autonomous engineersAI / ML Engineer
From datasetto deployed model.
Ana validates data, trains and evaluates models, deploys them behind A/B tests and watches for drift - so your ML team spends its time on the problem, not the plumbing.
Run #418 · illustrative
Epoch
40/40
Val F1
93.4
GPU budget
62%
✓Slice regression gate
✓p95 latency gate
✓Drift baseline set
Where ML teams lose time
The plumbing between a notebook
and a monitored model.
That is the work Ana takes on - so experiments multiply and models stay healthy after launch.
01
More experiments per week
Ana runs the data checks, feature work and sweeps in parallel, so your team spends its time choosing between results instead of producing them.
02
Models that stay healthy
Evaluation gates catch regressions before launch; drift monitors and scheduled retraining catch them after. Nothing quietly decays in production.
03
Works with your stack
PyTorch, scikit-learn, Hugging Face, MLflow, SageMaker, Vertex or Kubeflow - Ana works in the tools you already run and commits to your repos.
04
Lower compute bills
Early stopping, right-sized instances and drift-triggered retraining mean GPUs run when they move a metric - not on a calendar.
The whole lifecycle
From dataset to
monitored model.
Six stages, one run. Scroll through what Ana handles at each one.
Feature engineering
Generates and ranks candidate features against your target metric, then keeps the ones that earn their place.
Candidate generationRanking vs targetFeature selection
Training & sweeps
Selects algorithms and runs architecture and hyperparameter sweeps in parallel, within the GPU budget you set.
Model selectionHyperparameter searchBudget caps
Evaluation gates
Every release is gated on accuracy, latency and per-slice regressions; models are quantized or distilled to hit p95 targets.
Per-slice gatesLatency targetsQuantization
Canary rollout
Ships behind shadow traffic and a canary you control, with every model registered with its data, code and config lineage.
Shadow trafficCanaryLineage
Drift & retraining
Drift monitors watch production; retraining runs only when drift says it is worth it, and rollback is automatic past your threshold.
Drift alertsAuto-rollbackScheduled retraining
Inside the AI / ML Engineer
From raw data to a
monitored model.
Validation, training, evaluation gates, canary rollout and drift monitoring - in one run.
app.openana.ai/ml/experiments/exp-0412
AAna/AI / ML Engineer
Run · support-intent-v4exp-0412
- Validate dataset12,480 rows · schema OK · no leakage
- Engineer features38 features · 6 generated
- Fine-tune modelsupport-intent-v4 · 3 epochs
- Evaluate vs baselineF1 0.94 (+0.07) · p95 42ms
- Canary deploy10% traffic · shadow compare
- Enable drift monitoringPSI alert at 0.2
Training losstrainval
preparing data…
F1
-
Precision
-
Recall
-
p95 latency
-
Rolloutpending
v3 (current)v4 (Ana)
awaiting evaluation gate
LIVE
exp-0412 · epoch 3/3 · val loss 0.578
5 models2 GPUsp95 42ms
Key capabilities
The whole ML lifecycle,
handled.
Data preparation, feature engineering, training, evaluation, deployment and monitoring - end to end.
Feature engineering
Automatically identify, create, and select optimal features for your ML models.
Model selection
Intelligently select and evaluate the best algorithms for your specific use case.
Hyperparameter optimization
Automatically tune model parameters for optimal performance using advanced search strategies.
AutoML pipeline
End-to-end automation of the machine learning workflow, from data preprocessing to model deployment.
Continuous training
Automatically retrain models when performance degrades or new data becomes available.
Model versioning
Track and manage different versions of models, datasets, and experiments.
Deployment automation
Streamline model deployment across different environments and platforms.
Intelligent data processing
Streamline data preprocessing, cleaning, and feature extraction with AI-powered automation.
Model optimization
Automatically optimize model performance, resource utilization, and inference speed.
Models that stay healthy
Set the threshold. Ana holds the line.
Evaluation gates catch regressions before launch; drift monitors catch them after.
Feature drift (PSI) · 60 days · illustrativeThreshold 0.18
Ana rolls back on day 43.
You set the thresholds - F1, latency, drift. When production crosses one, Ana rolls back to the last good model automatically, then schedules retraining only when drift says it is worth it.
- Shadow traffic and canary before full rollout
- Automatic rollback past your threshold
- Retraining triggered by drift, not the calendar
Use cases
Where ML teams hand work to Ana.
Data Engineering
Accelerated data pipeline development and improved data quality.
- Built advanced data models and visualizations to drive data-driven decisions.
AI/ML Model
Developed and deployed sophisticated AI/ML models in weeks, not months.
- Achieved state-of-the-art performance with minimal human intervention.
ML Pipeline Automation
Automated feature engineering and model selection processes.
- Reduced ML model development time by 70% through intelligent automation.
- Optimized model performance and resource utilization automatically.
Notebook to pipeline
Turns exploratory notebooks into versioned, reproducible pipelines
- Generates and ranks candidate features against your target metric
- Runs architecture and hyperparameter sweeps within a GPU budget you set
Evaluation you can trust
Gates every release on accuracy, latency and per-slice regressions
- Quantizes and distills models to hit p95 latency targets
- Flags label leakage and train/serve skew before training starts
Safe model rollouts
Ships behind shadow traffic and a canary you control
- Registers every model with data, code and config lineage
- Rolls back automatically when F1 or drift crosses your threshold
GPU spend under control
Right-sizes training jobs and stops runs that stop improving
- Schedules retraining only when drift says it is worth it
- Reports cost per experiment and per served prediction
Governed by design
Autonomous, not unaccountable.
Ana works in your repos and your cloud, inside the limits you set.
What changes for the ML team
- Parallel experiment runs, every run reproducible from one ID
- Per-slice evaluation gates before every release
- Cost per experiment and per served prediction tracked
- Drift alerts with automatic rollback
What Ana never decides alone
- Changes land as reviewable pull requests in your repos
- Budget caps per experiment
- Every model registered with data, code and config lineage
- Promotion to production only past the gates you define
Why ML teams choose Ana
Faster iteration, models that stay healthy.
Accelerate ML model development with automated feature engineering, model selection, and hyperparameter tuning - the plumbing between a promising notebook and a monitored production model is the work Ana takes on.
More experiments per week
Parallel experiment runs, every run reproducible - your team chooses between results instead of producing them.
Models that stay healthy
Per-slice evaluation gates before launch; drift alerts with auto-rollback and scheduled retraining after.
Works with your stack
No new platform to migrate to - changes land as reviewable PRs in the tools you already run.
Lower compute bills
Budget caps per experiment and cost per prediction tracked; GPUs run when they move a metric.
Bring a dataset
Ship your next model with Ana.
Bring a dataset and a target metric - Ana takes it from validation to a monitored deployment.