What is MLOps?

1. What is MLOps?

Welcome to this MLOps course. My name is Folkert, and I'll be guiding you through the essential concepts and principles of MLOps.

2. Machine Learning Operations

MLOps is the abbreviation for Machine Learning Operations, and it describes the set of practices to design, deploy and maintain machine learning in production continuously, reliably, and efficiently. 'In production' means that we focus on machine learning that is used in business processes rather than just a machine learning model that we have locally trained on our laptop. MLOps is applied to what we call the machine learning lifecycle, which includes everything from design and development up until the maintenance of machine learning in production.

3. The origin of MLOps

MLOps originates from Development Operations, also called DevOps in short. DevOps describes a set of practices and tools that can be applied to software development to ensure that software is developed and deployed continuously, reliably, and efficiently. The development team that was developing software used to be separated from the operations team that released and maintained the software. MLOps extends these principles to machine learning, integrating data scientists and machine learning engineers into the development and operations cycle to streamline model deployment and monitoring. This ensures seamless and efficient machine learning workflows.

4. Why MLOps

In a real-world machine learning system, numerous components come into play beyond just the machine learning code. High-quality data is essential before we can train a model, and feature engineering is necessary to create useful inputs for training. After training, we need to thoroughly analyze the model's performance. Once the model is deployed in production, continuous monitoring is crucial to ensure it stays accurate and reliable over time. Key practices like MLOps are vital to ensure all these components integrate seamlessly and function effectively together.

5. Why MLOps

Inherent to MLOps is that it aims to bridge the gap between machine learning and operations teams, which enhances collaboration. Through MLOps principles we can automate the deployment of models, which reduces manual errors and speeds up the process of getting models from development to production. MLOps also facilitates monitoring of model performance, which helps to maintain accuracy and reliability over time. We will look into how MLOps aims to provide these benefits during this course.

6. Let's practice!

Now that you have had an introduction into the world of MLOps, let's dive into some exercises.

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