Oodles AI delivers enterprise-grade Automated Machine Learning solutions that automate the complete ML lifecycle. Our AutoML implementations use proven frameworks such as Auto-sklearn, TPOT, H2O.ai, Google Vertex AI AutoML, AWS SageMaker Autopilot, and Azure Machine Learning to reduce development time while improving model accuracy and reliability.
Automated Machine Learning (AutoML) is a technology-driven approach that automates model selection, feature engineering, hyperparameter tuning, evaluation, and deployment. AutoML enables organizations to build robust, production-ready machine learning models efficiently using standardized, repeatable pipelines.
Automated ML pipelines
Bayesian & genetic search
MLOps-ready outputs
Open-source & cloud AutoML
A structured AutoML workflow designed by Oodles AI to deliver accurate, scalable, and production-ready machine learning models.
1
Data Profiling: Automated analysis of schema, distributions, missing values, and data quality metrics.
2
Feature Engineering: Automated generation, transformation, encoding, and selection of predictive features.
3
Model Search: Evaluate multiple algorithms including gradient boosting, random forests, linear models, and neural networks.
4
Hyperparameter Optimization: Bayesian optimization and evolutionary strategies to maximize model performance.
5
Deployment & Monitoring: Model packaging, CI/CD integration, monitoring, and retraining workflows.
Auto-sklearn, TPOT, H2O.ai, Google Vertex AI AutoML, AWS SageMaker Autopilot, Azure ML
Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, PyTorch
Docker, Kubernetes, MLflow, CI/CD pipelines, REST APIs, cloud-native serving
Oodles AI delivers AutoML-powered solutions across predictive analytics, risk modeling, personalization, and operational intelligence.