<important> The rise of artificial intelligence has become omnipresent in recent years. In reality we see that only a small percentage of models makes it to production and stays so. In a series of blog posts on MLOps, we explain why and how companies can adopt MLOps practices to unlock the business value of AI. <important></important></important>
When it comes to a definition for MLOps, we believe Google’s definition is spot on:
“MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops).” - Google
MLOps is an umbrella term used to describe best practices and guiding principles that aim to make the development and maintenance of machine learning systems in production seamless and efficient. Simply put, MLOps is about automating machine learning workflows throughout the model lifecycle. Without MLOps, it will be a long and bumpy journey to operate models in production.
MLOps best practices try to:
Implementing MLOps typically means packaging commonly used Development & Operations steps into reusable components to speed up the development and deployment process and to limit human error.
These components typically include components that:
This helps provide a safe environment for team members to run experiments and train ML models. MLOps also encourages setting up a scalable ML model serving infrastructure and advocates the integration with infrastructure monitoring and log analysis tooling.
Similarly to Devops, MLOps is all about culture and practices - not about tools. A common mistake made is to directly dive into the realm of MLOps tooling, a world where it is easy to get lost. The tools should support the practices and not the other way around.
“MLOps ultimately drives Business Value”.
What is a model worth if it cannot be reliably deployed and supported in production for the intended usage? That’s right, models only create value once they’re in production.
As the model below illustrates, there’s a skew between where the intended business value is obtained versus where the majority of the initial development cost is made. If you don’t invest in bridging the gap to successfully deploy and operate models in production, you will probably be left behind disillusioned.
Currently, the real challenge with Machine Learning no longer lies with implementing and training ML models. The real challenge lies in building an integrated ML system (Dev) and to continuously operate it in production (Ops). Organizations that create machine learning solutions on an ad-hoc basis, without thinking about systemization, end up with experimental code that has to be rewritten entirely when they want to put the solution into production. In organizations where the data scientists don’t sit in the operations and production teams, we see that better collaboration is needed to eliminate inefficiencies and wasted code. So we see organizations turning to MLOps to increase the productivity of data science teams.
“Data scientists can implement and train an ML model with predictive performance on an offline holdout dataset, given relevant training data for their use case. However, the real challenge isn’t building an ML model, the challenge is building an integrated ML system and to continuously operate it in production.” — Google
Next to enabling the intended business value of the model, we see some additional key benefits related to MLOps:
In practice we see that MLOps brings indispensable value to the table, at the cost of bootstrapping your team with MLOps practices and supportive tooling. We recommend to consider it as soon as the potential value of a prototype or proof of concept has been proven. This will increase the success rate of machine learning projects in your organization.
This post is part of a series of blog posts on the topic of MLOps. In this series, we explain why and how companies can adopt MLOps practices to unlock the business value of AI. Find the other content here.