Everything you need for AI Performance in one platform.
We decided that Docs should have prime location.
Fundamentals of ML observability
Metrics, feature importance and more
We’re excited ???? to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
Seamlessly bridge the gap between business stakeholders, data science teams, and IT operations. Centralize all your models under one hub and easily scale your production ML.
Integrate Aporia with any ML infrastructure in under 7 minutes with a quick setup out-of-the-box and low to zero maintenance. Ensure the robustness and reliability of your models and their predictions.
In under 7 minutes deploy Aporia on your AWS, Google Cloud, Azure, or Databricks account. Centralize Connect directly to the data source where you store your models’ predictions, whether they’re on BigQuery, Snowflake, PostgreSQL, S3, or any other data store.
Integrate seamlessly with other MLOps tools, like Vertex AI, MLFlow, Sagemaker, and Kubeflow, and get a unified view of all your models in production.
Set up your Data Science and ML teams for success with intuitive tools to communicate, automate, and customize their production experience.
Gain complete control over your ML infrastructure through unparalleled centralization and standardization, making scaling your production ML a seamless and effortless process.
Maximize efficiency and focus on critical tasks with Aporia’s flexible, low-maintenance resource configuration. Pay only for what you use and enjoy peace of mind with zero-to-low maintenance.
Aporia’s high level of security ensures that sensitive data is protected, and a single source of truth for data reliability is maintained, providing a foundation for robust and accurate models.
This allows for easy tracking and traceability of production data, and eliminates the potential for inconsistencies or errors caused by using multiple sources of data.
