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Auto-PyTorch vs Obviously AI

Auto-PyTorch

automl.github.io

Obviously AI

obviously.ai

Side by side

FigureAuto-PyTorchObviously AI
Price fromFreeFree
Free planYesYes
Free trial—Yes
Open source——
Web—Yes
LinuxYes—
API—Yes
Self-hostedYes—
Plans published14
Spec score · AutoML Software8.4 (#5)8.0 (#10)
DeploymentThe project provides a Docker image and can be installed from PyPI or manually in a Python environment.The product page says instant web apps let users share a dynamic link to use a model in real time.
IntegrationsThe documentation describes using Dask.distributed for parallel Bayesian optimization and sklearn column transformers for data preprocessing.The official integrations catalog lists Airtable, Amazon Redshift, Box, Chartio, CSV, Dropbox, Google Analytics, Google BigQuery, Google Drive, HubSpot, JSON, Kaggle, Mode, MongoDB, MySQL, PostgreSQL, Salesforce, Shopify, Snowflake, SQL Server, Tableau, Typeform and Zapier.
What it doesAuto-PyTorch is an automated machine-learning toolkit based on PyTorch that helps users automate algorithm selection and hyperparameter tuning.Obviously AI is a no-code hosted platform for turning historical data into predictive machine-learning models without programming.

Plans

Auto-PyTorch

  • Auto-PyTorchFree

Auto-PyTorch pricing →

Obviously AI

  • FreeFree
  • Enterprise PlanPriced on request
  • SMB PlanPriced on request
  • Startup PlanPriced on request

Obviously AI pricing →

In their own words

Auto-PyTorch

Auto-PyTorch is a free, self-hosted machine-learning toolkit based on PyTorch. It automates algorithm selection and hyperparameter tuning, including neural-network architecture and training settings, within a user-defined search budget. Documented search methods include Bayesian optimisation, meta-learning, SMAC and Hyperband. Supported work includes tabular classification, tabular regression and time-series forecasting. Tabular preprocessing can perform imputation, categorical encoding, scaling and feature processing, while related hyperparameters are tuned. The toolkit can build ensembles from validation-set predictions, with controls for ensemble size and candidate limits. Users can set estimator memory limits and total wall time. Parallel Bayesian optimisation uses Dask.distributed; workers need shared filesystem access to training data and models. Installation documentation specifies Linux, Python 3.7 or later, a C++11-capable compiler and SWIG 3.0. SWIG 4.0 or later is unsupported. Forecasting needs extra dependencies. A Docker image and PyPI installation are documented, under the 3-clause BSD license.

Visit automl.github.io

Obviously AI

Obviously AI is a freemium, web-based no-code tool for turning structured data into predictive models. Listed model uses include classification, regression, and time series prediction. Models can be published as instant web apps with shareable dynamic links, or connected through real-time REST APIs that send predictions to tools such as Power BI and Looker. Automatic model monitoring and Zapier if-this-then-that automations are also described. Integrations include Amazon Redshift, Google BigQuery, Google Drive, HubSpot, Shopify, Snowflake, Tableau, and Zapier. A structured dataset is required; the documentation recommends at least 1,000 rows and 5 columns. CSV is the listed import format, with uploads up to 10 MB. The Free plan costs nothing and includes 1,200 predictions, one user seat, unlimited models, 1M rows of training data, CSV-only data, and limits of 10,000 data rows. Obviously AI is based in San Francisco. Security information lists SOC 2 Type II, GDPR data-retention compliance, HIPAA compliance, TLS in transit, and AES-256 at rest.

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