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.

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THE MAIN SEQUENCE SOFTWARE ECOSYSTEM

What is Jarvis?

A unified framework for machine learning as code.

It’s a unified framework for integrating ML and AI libraries

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.

It’s machine learning as code: Empowering scalable and transparent AI development

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.

It’s scalable AI training

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.

It’s efficient use of resources

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.

THE FEATURES

Scalable Artificial Intelligence  & Machine Learning

Machine Learning as code
Decrease experiment iteration time
Build advanced model pipelines
Unified Frameworks
Build for training in scale
Model governance

Machine learning as code

Machine learning training and serving properly defined

Facilitates rapid experimentation and deployment of machine learning models across diverse environments.
Ensures consistent results through precise documentation and version control of models.
Promotes openness and collaboration by codifying the entire AI pipeline, making it accessible and understandable to stakeholders.

Scalable Training

Train and scale like a pro

Efficiently train AI models at scale by supporting parallel processing and distributed computing.
Integrate with cloud infrastructure to seamlessly scale computational resources, enabling efficient handling of large datasets and complex models while optimizing costs and enhancing flexibility for rapid experimentation and deployment.

Unified AI and ML libraries

Same experiment, any library

Provides a unified interface across different machine learning libraries, ensuring consistent experimentation methodologies and results interpretation.
Streamlines workflow by reducing the need to switch between disparate libraries, thereby saving time and effort in model development and evaluation.

Efficient AI training

Train efficiently, save  time, save  money

Meticulously designed to maximize resource utilization, ensuring optimal efficiency in computational and storage resources.
Ray integration dynamically allocates resources based on workload demands, utilizing cloud infrastructure and optimizing hardware utilization.
Robust monitoring and management capabilities provide real-time insights into resource usage, enabling proactive adjustments to enhance efficiency and reduce costs.