Deep Variance Autopilot.
The end-to-end AutoML pipeline. Automatically infer types, clean data, engineer features, and train the best-fit ML or deep learning model. Powered by LLM-driven code generation and intelligent multi-model comparison.
7
Automated pipeline stages
1 Call
From raw data to trained model
8+
Model architectures ranked
Core Optimization
Integrated directly into your runtime to automate efficiency without changing your training code.
Intelligent Data Processing
Automatically infer column types, encode categoricals, and extract structural patterns to guide intelligent preprocessing decisions.
- LLM-driven type inference
- Categorical encoding
- Correlation & MI analysis
LLM-Driven Preprocessing
Self-correcting LLM code generation handles missing data and feature engineering. Intelligent sampling selects representative subsets efficiently.
- Missing data handling
- Intelligent sampling
- Automatic retry on error
Automated Model Selection
Evaluates and ranks classical ML and deep learning architectures per run, returning a full leaderboard and per-feature importance scores.
- Ranked model leaderboard
- Feature importance scores
- Classical ML & deep learning
Pipeline Intelligence
Self-Correcting Generation
If LLM-generated code fails, the error is automatically fed back as context and retried up to 3 times, without any manual intervention.
Full Run Observability
Every pipeline run returns per-stage wall-clock timing, peak memory usage, and CPU metrics for complete visibility into the automated process.
[1/7] AutoCastLayer ✓ 1.2s
[2/7] DataProfilingLayer ✓ 6.8s
[3/7] CorrelationLayer ✓ 14.2s
[4/7] SamplingLayer ✓ 3.1s
[5/7] PreprocessingLayer ↻ retry 1/3 → ✓ 22.7s
[6/7] ModelRecommendationLayer ✓ 8.3s
[7/7] ModelTrainingLayer ✓ 4m 18s
accuracy: 92.3% | f1_macro: 91.1%
peak_mem: 512 MB | total_time: 5m 14.3s
Python-first integration
Integrate Deep Variance into your existing training scripts with just a few lines of code.
Install via pip: deepvariance-sdk
Point it at your dataset and target column
Get a trained model, full metrics, and ranked leaderboard
from deepvariance.pipelines.ml import MLPipeline from deepvariance.typings import PipelineConfig import pandas as pd data = pd.read_csv("customers.csv") config = PipelineConfig(dv_api_key="dv_...") # Run the full automated pipeline pipeline = MLPipeline(config=config) result = pipeline.run(data, target="churn") print(result["metrics"])
Works with your stack
Autopilot connects to any data source and runs on any infrastructure, cloud or on-premise.
Tabular & Image Data
Cloud & Data Providers
Extensible by design
Any data source, any format. Implement a simple interface and Autopilot handles the rest: batching, prefetching, and distributed loading included.
- Custom S3-compatible stores
- Proprietary database connectors
- Streaming and real-time sources
Deploy Anywhere, Own Everything
Runs entirely on your hardware. Your data, your models, your infrastructure. No cloud dependency, no data egress, no vendor lock-in.
On-Premise Execution
The entire pipeline, from data profiling to model training, runs on your own servers. No data ever leaves your environment.
Zero Data Egress
Training data is never transmitted to external services. LLM calls carry only schema metadata and error traces, never raw records.
Sandboxed Code Execution
LLM-generated preprocessing code runs in a restricted sandbox. Only approved libraries are permitted with no arbitrary system access.
Bring Your Own LLM Key
Supply your own OpenAI or Groq API key via PipelineConfig. You choose the provider, you own the usage.
Full Audit Trail
Every pipeline run emits per-stage timing, memory usage, and CPU metrics: a complete, inspectable record of every automated decision.
Industry Agnostic
Works on any tabular dataset regardless of domain: healthcare, finance, retail, or manufacturing. No industry-specific configuration needed.
Request a Demo
See Autopilot in action on your data. Our team will walk you through a live session.