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 […]
Simply put, Explainable Artificial Intelligence (XAI) allows machine learning algorithms to create outputs and results that can be understood and trusted by humans. A model that is explainable AI describes its impact, biases, and the expected impact of the model. With its help, AI-powered decision-making can be assessed for fairness, transparency, and accuracy.
When putting AI models into production, building trust and confidence is crucial for an organization. An organization can likewise use AI explainability to adopt a responsible AI development approach.
Understanding the process that led an AI-enabled system to produce a specific outcome is very beneficial. In addition to helping developers monitor a system’s performance, explaining how a decision was made is important for meeting regulatory standards and allowing those affected to challenge the decision.
