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.
Part 1: ML Observability fundamentals
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.
Part 2: World of MLOps
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.
Topics learned in this course
You’ll need basic understanding of the following:
- Machine Learning
- Python
- MLOps
