Our primers offer compact and practical insights into key IT topics. In this edition, we explore the challenge of deploying and operating machine learning (ML) systems in production.
ML systems consist of more than just the trained model; they also include the training data and the code used to build and deploy them. Bringing these components together into a reliable, production-ready system requires the right processes and tools. This is where MLOps — an extension of DevOps for ML — comes in.
In this primer, Anja Kammer, Alexander Kniesz, and Dr. Larysa Visengeriyeva introduce the core concepts of MLOps and share actionable guidance on:
- Building data engineering pipelines to prepare and validate ML data.
- Designing scalable training and deployment pipelines for ML models.
- Applying MLOps best practices like automation, testing, and monitoring to ensure reliability.
This primer is ideal for engineers, architects, and team leads aiming to improve the development and operation of ML systems. It offers hands-on strategies to streamline deployment, foster team collaboration, and build sustainable workflows.
You can download the primer free of charge as a PDF.
Our primer provides software developers and data scientists with practical guidance on the end-to-end ML engineering process. Readers will learn best practices for creating robust ML projects using state-of-the-art frameworks while adhering to software engineering principles, covering everything from data engineering pipelines to ML workflows and monitoring in production environments.
Anja KammerCo-author, Senior Consultant at INNOQ
