Functions, Users, and Comparative Analysis
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Build AI products you can trust.
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 […]
Introduction >
Welcome to the Aporia ML Observability Basics Academy! This program is designed to provide machine learning engineers, MLOps engineers, data scientists, and other ML practitioners with a comprehensive, high-level understanding of observability in the context of machine learning. Throughout this academy, we will explore various use cases, including recommender systems, dynamic pricing, NLP, and more.
In the first part of the program, we will cover the fundamentals of ML observability, including monitoring model performance, data drift, and model biases. We will then introduce you to essential machine learning tools and practices to seamlessly integrate observability into your ML pipelines.
The second part of the academy will delve into the world of MLOps. Join us as we unlock the power of observability in ML and empower you to build trustworthy and high-performing machine learning systems.
Don’t feel like you can finish all the lessons in one day? No problem, put us on your calendar for quick and easy reminder here.
You’ll need basic understanding of the following:
