Sold by: Synthefy
Deployed on AWS
Synthefy-Nori is a foundation model for tabular data that replaces the train-and-tune workflow: pass your labeled rows as context and get predictions in a single forward pass - no training, tuning, feature engineering, or cross-validation. It achieves the highest mean R2 of any tabular foundation model across 96 public regression datasets and beats tuned gradient boosting (XGBoost/LightGBM) on 9 of 13 TabArena tasks, at just 6M parameters. It deploys as an Amazon SageMaker endpoint that runs entirely within your own AWS account.
Overview
Synthefy-Nori is an in-context learning foundation model for tabular regression. Instead of building and tuning a separate model for every dataset, you pass your labeled rows (X_train, y_train) as context and the rows you want scored (X_test) in a single request, and predictions come back in one forward pass. No gradient updates, no hyperparameter search, no validation sweep. The model handles preprocessing, high dimensionality, missing values, and skewed targets on its own, so you can hand it raw rows. When your data drifts, you simply send the new rows as context; there is nothing to retrain.
Across 96 regression datasets from three independent sources (TabArena, TALENT, and OpenML-Reg), Synthefy-Nori achieves the highest mean R2 of any tabular foundation model. With zero tuning it beats tuned gradient-boosting baselines (XGBoost and LightGBM, AutoGluon best-quality) on 9 of 13 TabArena tasks. It delivers this at 6M parameters, roughly a tenth the size of comparable tabular foundation models, and returns predictions for typical small-to-mid tables in about a second on a single GPU.
Synthefy-Nori is delivered as an Amazon SageMaker model package that you deploy and run inside your own AWS account, so your data never leaves your environment. It is permissively licensed (Apache 2.0) for commercial use, and its scikit-learn-style fit/predict interface drops into existing pipelines.
Highlights
- Replaces the train-and-tune loop - no training, hyperparameter search, feature engineering, or cross-validation. Pass labeled rows as context and get predictions in one forward pass; when data drifts, just send new rows.
- Best-in-class accuracy at 6M parameters - the highest mean R2 of any tabular foundation model across 96 public regression datasets, and beats tuned gradient boosting on 9 of 13 TabArena tasks.
- Runs in your account - deploys as an Amazon SageMaker endpoint so your data never leaves your AWS environment, with a simple JSON API: send X_train, y_train, and X_test to get one prediction per row.
Details
Sold by
Categories
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Vendor refund policy
Synthefy-Nori is offered free of charge on AWS Marketplace, so there are no software fees to refund. Buyers pay only their own AWS infrastructure costs (for example, SageMaker instance charges), which are billed directly by AWS and are outside Synthefy's control. For any questions, contact support@synthefy.com .
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial AWS Marketplace release of Synthefy-Nori, an in-context learning (ICL) foundation model for tabular regression.
Model
- ~6M-parameter tabular foundation model. Given a few labeled rows as context, it predicts on new query rows in a single forward pass, with no task-specific training or fine-tuning.
- Trained entirely on synthetic data; handles preprocessing, high dimensionality, missing values, and skewed targets internally.
Capabilities
- Returns point predictions with scikit-learn-style fit/predict semantics, exposed as a simple JSON contract: send X_train, y_train, and X_test; receive one prediction per query row.
Performance
- Across 96 public regression datasets from three independent suites (TabArena, TALENT, OpenML-Reg), under a fixed deterministic protocol: mean R2 0.7506, median R2 0.8702.
Additional details
Inputs
- Summary
Synthefy-Nori is an in-context regression model: each request supplies the labeled context rows plus the query rows to predict, and the model returns one prediction per query row in a single forward pass. There is no separate training step - the labeled data is passed in with every request as context.
Content type: application/json
Request body (a JSON object with these fields):
- X_train: array of arrays - the labeled context rows, shape [n_context][n_features]. Numeric values only.
- y_train: array of numbers - the regression target for each context row; length n_context, aligned by position with X_train.
- X_test: array of arrays - the rows to predict, shape [n_query][n_features]. Same feature columns, in the same order, as X_train. Numeric values only.
- task: optional string; "regression" (the default and only supported value).
Requirements:
- Features must be numeric. Encode categorical columns (one-hot or ordinal) before sending. Missing values may be sent as null and are handled by the model.
- X_train and X_test must have the same number of features in the same column order.
- y_train must align 1:1 with X_train by row position.
- Real-time invocations are limited to 6 MB and 60 seconds; use batch transform for large tables.
- Input MIME type
- application/json
{"task":"regression","X_train":[[0.0,1.0],[1.0,0.0],[0.5,0.5],[0.2,0.8]],"y_train":[0.1,0.9,0.5,0.3],"X_test":[[0.3,0.7],[0.8,0.2]]}
{"task":"regression","X_train":[[0.0,1.0],[1.0,0.0],[0.5,0.5],[0.2,0.8]],"y_train":[0.1,0.9,0.5,0.3],"X_test":[[0.3,0.7],[0.8,0.2]]}
Support
Vendor support
Support is available by email at support@synthefy.com . We assist with deployment, invocation, and functional questions on a best-effort basis, typically responding within two business days.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
Foundation model for time series forecasting with contextual metadata.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.