Functions, Users, and Comparative Analysis
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We’re super excited to share that Aporia is now the first ML observability offering integration to the Databricks Lakehouse Platform. This partnership means that you can now effortlessly automate your data pipelines, monitor, visualize, and explain your ML models in production. Aporia and Databricks: A Match Made in Data Heaven One key benefit of this […]
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 […]
An ML Platform or machine learning platform is a module providing the user with a system that manages the modeling lifecycle, focusing on experimentation, reproducibility, and deployment.
During the research phase, a data scientist tests different hypotheses using a variety of features in order to achieve the best results. In order to manage experiments, data scientists can use an ML Platform for reusable components which can be inserted into any Python script and also provides specific interfaces for different frameworks like TensorFlow or Scikit-Learn
ML Platforms will implement faster insights and greater business impact if used by a team. They can optimize a company’s machine learning efforts by implementing MLOps and enhancing insights.