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.
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Refer to Google Colab for code snippets. When it comes to the world of data science, machine learning models usually...
Ignacio Amaya de la Peña
Introduction Machine learning algorithms, specifically in NLP, LLM, and computer vision models, often deal with high-dimensional and unstructured data, such...
Niv Hertz
Fraud detection is a mainstream machine learning (ML) use case. In recent years, the demand for AI-powered fraud detection systems...
Noa Azaria
*Google collab with code snippets here. **Notebook tests use simple dummy data, not to simulate real-life data, but to demonstrate...
Ignacio Amaya de la Peña
Measuring the performance of ML models is crucial, and the ML evaluation metric – Recall – holds a special place,...
Or Jacobi
Introduction Accurately evaluating model performance is essential for understanding how well your ML model is doing and where improvements are...
Igal Leikin
A few minutes of configuration, for an endless level of security and reliability for your AI Agent.
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Today’s spotlight is on Root Mean Square Error (RMSE) – a pivotal evaluation metric commonly used in regression problems. Through...
Or Jacobi
Today we’re going to delve into a vital metric called Mean Absolute Percentage Error, or MAPE for short. Understanding MAPE...
Noa Azaria
Understanding evaluation metrics is a crucial aspect of creating effective machine learning models. One such metric is the Precision-Recall AUC...
Tom Alon
This guide will guide you through the challenges and strategies of monitoring Large Language Models. We’ll discuss potential model pitfalls,...
Niv Hertz
During their extensive training and learning process, machine Learning engineers will inevitably encounter ML bias and fairness questions, which casts...
Yaniv Zohar
In the world of Machine Learning (ML) and information retrieval, ranking models are integral. Evaluating and improving the performance of...
Tom Alon
