Customize the pricing plan as per your requirements.
Looking for ML Support?
You can subscribe to our on-demand ML support plans to help you in various ML Ops activities.
Starting from 40hrs/week
All Professional plan features, plus
Making sense of monitoring alerts
Troubleshoot your ML Models
Course correction of AI/ML usage
Assisting in model/data debugging
Representation in ML discussions - internal/external
Option to take dedicated support
You've got questions. We've got answers.
Absolutely! If you are looking to explore AryaXAI before onboarding with either a personal or professional plan, contact us for a customized demo and start exploring the platform based on your specific requirements.
AryaXAI was built with the intent of sharing AI knowledge and experiences across teams, both technical and business. Data Science, ML and business teams can easily monitor model performance, metrics, fairness and explainability through visual dashboards and reports, fostering participation from diverse stakeholders.
Yes! You get the flexibility of deploying AryXAI on-premise or on your cloud. Connect with our sales team to know more details.
Arya offers customized subscriptions and pricing plans as per your requirement. Pay only for what you use!
We understand our customer's data protection needs and offer the most comprehensive set of expertise and services to help you protect your data. Arya offers flexible deployment options, and you get to manage the privacy controls of your data.
An MLOps platform aims to deploy and maintain ML systems in production. Observability tool covers a larger scope compared to MLOps - it understands why the problem exists, and the best way to resolve it. It helps to understand the why and figure out what needs to be done to resolve the problem.
An MLOps platform is focused on keeping the ML model development lifecycle functional. ML observability goes beyond just monitoring and brings a proactive approach to investigating model issues and highlighting the root cause of the problem.
Prediction is an information output that comes from entering some data and running an algorithm. This output is the result of training the algorithm on a historical dataset and applying it to new data when forecasting the likelihood of a particular outcome.
Yes! From open-source frameworks to ML platforms, AryaXAI can work with any ML stack.
Absolutely! You can customize the pricing plan as per your requirements.
Yes! AryaXAI combines ML observability, monitoring, governance, explainability and provides necessary user controls to build trust and confidence in the model.
Why choose AryaXAI?
An advanced ML Observability framework for mission-critical use cases.
ML Monitoring: Go beyond simple alerts
Troubleshoot your models quickly and precisely
All alerts are not critical. Real-time ml model monitoring surfaces critical issues with data or model performance so you can troubleshoot root causes quickly and precisely instead of focusing on every other alert.
Swiftly troubleshoot and remediate issues with your ML models through accelerated root cause analysis across thousands of predictions and features.
Configurable safeguard controls to create customized thresholds and alerts for the model as per your risk profile.
Quickly detect where issues emerge and troubleshoot root cause for model performance deviation, model bias, data drits etc. all in one place!
Explain your models accurately
Unlike approximating your model functioning, provide accurate explanations
Decode the ‘true-to-model’ feature weightage along with node and layer level weights for any neural network with detailed explanations.
Different users need different types of explanations. AryaXAI offers a variety of explanations to help your stakeholders understand and empathize with model performance.
Get near real-time explainability of your model, such that you can offer it along with your predictions to the end users.
AryaXAI is built for diverse stakeholders. Whether you are an ML engineer, Data scientist, Compliance office Business leader or Audit executive, AryaXAI supports and allows participation from diverse stakeholders
ML Audit: Productized approach to achieve scale
Identify potential risks and develop safeguard controls to avoid them
A centralized repository enables tracking, reviewing and retracing of various model-related artefacts making it easy to have a 360-degree view of the solution, thereby providing a scope to do a complete audit.
Automatically generate risk reviews and compliance audits with audit trails to support compliance, data science and business teams.
Understand the model’s state at the time of prediction with case-wise traceability. Retrace the model’s metadata, output and condition for a prediction.
Collaborate securely with role-based audit process flows and record individual observations/consents.
Policy Controls: Protect from AI risks
Ensure effective governance across every step of the development lifecycle
Easy-to-use UI puts advanced controls at the fingertips of risk owners, to detect and define policies that can prevent AI errors preemptively or ensure adherence to guidelines.
Easily add/edit/modify policies with customizable controls to maintain and modify policies. Version control your changes and productionize the preferred version.
Define responsible requirements across a variety of policies with contextual policy implementation on data or model outputs or both.
Configure, manage and productionize policies with a single click through policy orchestrator UI.
Schedule a demo on how AryaXAI can deliver AI Governance, acceptance of AI solutions and scale with confidence.