The most advanced ML Observability platform
Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more difficult. However, they both, similarly, include many different parts that help create value when put together. As every organization sets out on a unique path to building its own machine learning platform, taking on the project of building a […]
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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 […]
Aporia is the perfect partner for MLOps and ML Platform engineers looking to bridge the gap between data science teams and IT operations.
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.
