How to maintain ML models once after you deploy them, and what exactly to prepare for? In this guide, we look into the key concepts that relate to production ML model operations.
This guide is made for data scientists, data engineers, ML engineers, and AI product managers who deal with operating production ML-based systems and products.
What you will find in this guide:
- Deep dives into topics that relate to production model use and MLOps, including model monitoring, model retraining, and addressing data and concept drift.
- Illustrated explanations. Our guide has a lot of visuals, making it easy to follow along and understand each aspect of machine learning in production, even if you are new to the topic.
- Pragmatic focus. We steer clear of academic jargon and focus on real-world challenges faced by ML practitioners instead. The goal of this guide is to explain the issues you might face in your daily work and provide practical solutions.
- Modular approach. No need to commit to the entire guide at once. Each article is self-contained, allowing you to choose a topic that aligns with your current needs and interests. You can dive in from any starting point.
