Seamless machine learning model management, deployment and monitoring for supercharging MLOps for any organization on the best AI platform
Use navio to perform various machine learning operations across an organization's entire artificial intelligence landscape. Take your experiments out of the lab and into production, integrate machine learning into your workflow for a real, measurable business impact.
navio provides various Machine Learning operations (MLOps) to support you during the model development process all the way to running your model in production. Automatically create REST endpoints and keep track of the machines or clients that are interacting with your model.
Have a look at how navio works
Why you should run your models on navio
Focus on exploration and training your models to obtain the best possible result and stop wasting time and resources on setting up infrastructure and other peripheral features. Let navio handle all aspects of the productionisation process to go live quickly with your machine learning models. Security, monitoring, deployment, and containerization all come as standard with simplicity at the center of everything.
Upload any custom python-compatible model to navio to make use of its user interface to easily manage, deploy and integrate your models. navio makes use of MLflow, a standard for packaging ML models so that you can continue to create models using your favourite tools and deep learning frameworks.
Use REST endpoints to integrate model predictions into any application, machine, or device. Effortlessly replace your model with a retrained version without having to regenerate the endpoints or reconfigure access, speeding up collaboration between departments and ensuring an efficient workflow.
Deploy models without the need for technical skills. Manage multiple deployments and secure access to your deployed models with built-in security features and access control.
Monitoring and performance
Monitor deployed models to ensure robustness and model stability. Prevent data drift with out-of-distribution detection and keep track of real-time changes in incoming data and predictions.
Retrain your models within navio when more data becomes available. Manage all your retrained models in one use case and switch them out easily to compare results.
Have an overview of all devices connected to your deployed models. From mobile phones sending gyroscope data to your model to any industrial machine on the shop floor requesting predictions with its sensor data.
Deploy Models at the Edge
navio has partnered up with TTTech Industrial to offer users the ability to deploy their machine learning models directly to edge devices. Integrated with the Nerve Management System, users can now directly execute their models on their connected Nerve devices. Connect to data streams produced by machines or sensors via the Nerve’s data gateway and use these as input to get predictions in real-time.
This results in
improved security and
Leverage navio across any industry to gain a competitive advantage by applying AI in real-time. Whatever your use case, navio helps you productionise your anomaly detection, computer vision, or deep learning models for a real return on investment.
More than just deploying a model
Organization and Simplicity
Stay organized by managing models in various workspaces and use cases. Control access via user management.
Out-of-distribution detection (OOD) for detecting outliers in the data during prediction.
Speed up model inference and get real-time predictions faster than ever before with GPU acceleration. Performing computations on a GPU versus a traditional CPU does not only benefit performance but also tends to be more energy-efficient.
Available as an on-premise solution or on Azure cloud.
The navio API allows data engineers and data scientists to programmatically include navio in their development or production workflow. Use the API to perform various tasks including training, uploading, and deploying.
Integration of our labelling tool
Annotate your data for object detection to increase the accuracy of your model
Support managing of ONNX models
Extending retraining with fine-grained version management