A unified framework for machine learning as code.
With model governance, Jarvis integrates major ML and AI libraries to train, monitor, experiment, persist and serve a diversity of AI and ML models.
This framework provides a unified approach to seamlessly integrate ML and AI libraries. Whether you're building experiments using scikit-learn for regression, implementing a PyTorch neural network, or developing a LightGBM boosted tree model, this framework is a cohesive environment for experimentation and deployment.
Machine learning as code represents a paradigm shift in AI development, where algorithms and models are defined and managed through code rather than traditional manual processes.
Designed to efficiently train AI models at scale, addressing the complexities and demands of modern data environments. It supports parallel processing and distributed computing, allowing for the simultaneous training of multiple models or large datasets.
The efficient AI training system maximizes resource utilization by employing advanced parallel processing and distributed computing techniques. It accelerates model training while maintaining accuracy by dynamically allocating cloud infrastructure and optimizing hardware usage.
Machine learning training and serving properly defined
Train and scale like a pro
Same experiment, any library
Train efficiently, save time, save money