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Octai Model standardizes your ML project structure and automatically generates versions for data, code and parameters on every model build. Deliver immutable & tested production grade artifacts continuously.
Automated Machine Learning
AutoML is pervasive across Octai . Powering everything from feature transformation to model selection, monitoring and deployment, robust autoML capabilities are the engine behind our ability to deliver AI that does AI.
When you build and train your model, you can build alternative experiments in parallel. Giving you the chanve to compare and contrast the results, without having to wait.
Increase accuracy, ROI and time savings with optimization across all components of the machine learning modeling pipeline - delivered through a mix of our proprietary and innovative state-of-art parameter optimization methods.
Adopt multiple levels of both fully automatic and easily customizable ensembling to increase accuracy and ROI.
Assess model robustness and mitigate risks in production by obtaining a holistic view of the models and preventing failures on new data.
Unsupervised Automatic Machine Learning
Immediately get new insights on your unlabeled data with unsupervised techniques such as clustering to automatically group topics, outlier detection to identify irregularities in your data, and dimensionality reduction to reduce model overfitting and complexity.
Leaderboard for Forecasting
Save time getting an optimized forecasting model with a new leaderboard mode, specific to time series experiments. Automatically design and run multiple experiments with varying amounts of pairs (train-test gap, forecast horizon, etc.) to help with model selection.
Identify areas in your data where a model shows bias across various metrics with a dashboard that shows disparity between groups in the dataset.
Automatic Feature Engineering
Increase accuracy and ROI with our proprietary grandmaster-level feature engineering that automatically extracts predictive statistical information from your data for highest accuracy and for gaining actionable insights into the causal nature of the data.
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