Automated Machine Learning Platform for Business Teams
Automated machine learning shouldn't stop at hyperparameter tuning. OctOpus automates the entire ML workflow: data profiling, feature engineering, model selection, training, validation, deployment, and monitoring. It's the AutoML platform business teams reach for when DataRobot is too expensive, H2O is too engineering-heavy, and SageMaker is too cloud-locked.
Beyond hyperparameter sweep
Classical AutoML automates step 3 of a 7-step workflow. OctOpus automates all 7: domain understanding, data quality probe, leakage detection, feature engineering, model search, calibration, deployment. The team uploads a CSV and gets back a production-ready prediction endpoint with full documentation.
Models you don't have to know about
Catboost, LightGBM, XGBoost — battle-tested gradient boosting. TabPFN — foundation model that beats fine-tuned baselines on small datasets. NeuralForecast (xLSTM, PatchTST, TFT), Chronos, TiRex, TimesFM — modern forecasting. HuggingFace transformers for NLP, ResNet for images. The agent picks; you don't have to know any of these acronyms.
Why teams switch from incumbent AutoML
DataRobot starts at $150k/year and locks data into a proprietary format. H2O Driverless AI requires a Java tuning expert in the room. SageMaker Autopilot only runs on AWS and only on tabular. OctOpus runs on any cloud, supports every modality, and the free tier handles real workloads. Pro plan is $49/month.
Key capabilities
- Zero-config — drop a CSV, the agent does the rest.
- Supports tabular, time series, NLP, images out of the box.
- Self-hosted or BYO-cloud (AWS Bedrock, Azure, on-prem).
- Free tier: 6 experiments per session, no credit card.
- Pro: $49/month, 50 experiments per session, unlimited datasets.
Frequently asked questions
What's the difference between AutoML and OctOpus?
Classical AutoML automates step 3 of a 7-step workflow (hyperparameter tuning). OctOpus automates all 7: domain understanding, data-quality probe, leakage detection, feature engineering, model search across families, calibration, deployment. Output is a production prediction API, not a leaderboard CSV.
Which AutoML alternatives is OctOpus competing with?
DataRobot ($150k+/yr, locks data in proprietary format), H2O Driverless AI (Java tuning expert required), SageMaker Autopilot (AWS-only, tabular-only), Google Vertex AI AutoML (GCP-only). OctOpus runs on any cloud, supports every modality, free tier handles real workloads, Pro is $49/month.
Can it handle time series?
Yes. NeuralForecast (xLSTM, PatchTST, TFT) for panel data with covariates; Chronos / TiRex / TimesFM zero-shot for short horizons; LightGBM with lag features for noisy retail. Always uses chronological train/holdout split, never random — random splits on time-series data is the #1 silent failure mode in classical AutoML.
How fast can I get a model?
5-10 minutes for most tabular classification / regression problems. Time-series and large datasets can take longer. The free tier runs 6 experiments per session, Pro plan runs up to 50.