Jupyter Notebook to Production in Seconds
Quickly turn your Jupyter Notebook into a production-ready ML model. No need for extra code or infrastructure. Focus on building your models and let Kore handle the rest.
Quickly turn your Jupyter Notebook into a production-ready ML model. No need for extra code or infrastructure. Focus on building your models and let Kore handle the rest.
Effortlessly convert your Jupyter notebook into a production-ready Docker image, complete with a robust REST API. Deploy your machine learning models anywhere Docker runs, ensuring portability, scalability, and seamless integration with your existing infrastructure. No manual code refactoring required - just focus on your data science.
Leverage our fully managed SaaS platform to deploy your models with a single click. Enjoy automatic scaling, high availability, and secure hosting without the hassle of managing servers. We ensure your models are always ready to handle production workloads, from a handful of requests to enterprise-scale traffic. Monitor your models in real time with built-in analytics, logging, and alerting, and enable continuous model retraining for optimal performance and reliability.
Kore automatically cleans and optimizes your notebook code for production, removing unnecessary cells and dependencies. Stay confident in your model's performance and reliability.
Boost your model's accuracy with automated hyperparameter tuning and neural architecture search. Kore intelligently explores better configurations and architectures, helping you achieve state-of-the-art results without manual experimentation. Save time and maximize your model's potential.
Whether you're building with scikit-learn, TensorFlow, PyTorch, or the latest generative AI frameworks, Kore supports the full spectrum of machine learning. Deploy classical models, deep neural networks, and generative AI solutions with equal ease, all from your notebook.
Gain insights into your model's decisions with built-in feature explainability tools. Kore provides clear, interpretable explanations for model predictions, helping you build trust with stakeholders and meet regulatory requirements for transparency in AI.