<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>

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An intro to MLOps

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 

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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.

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MLOps Best practices

MLOps best practices try to:

1. Make data extraction, model deployment and monitoring as visible as possible to make it easier to work in a collaborative way
2. Make it easy to audit and reproduce production models by storing all model training related artifacts (e.g. versioning data, parameters and metadata)
3. Make the retraining of a model as close to effortless as possible
4. Test and monitor the machine learning systems 
   

Implementing MLOPs

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:

• Version data & ML models
• Check for data bias, skew and schema changes
• Check ML model accuracy and ML model behaviour
• Deploy ML models to production if the ML model behaves within the specifications

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.

Why should you care?

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“MLOps ultimately drives Business Value”.

Sven Degroote

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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.

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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.

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“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

Key benefits of MLOps

Next to enabling the intended business value of the model, we see some additional key benefits related to MLOps:

1. Increased productivity of data science teams
   Through simpler processes to update existing models, more development time for novel models and less time spent to reproduce models. Efforts can be focused on developing new models, rather than maintaining existing ones. 
2. Improved safety of the developed Machine Learning solutions
   Bugs are prevented, there is a lineage of data and model artifacts and standardization. The ML models deployed to the production environment are automatically checked for ML model bias, data skew and ML model behaviour. In this sense, MLOps is a guarantee for legally compliant ML in production.
3. Happier machine learning engineers
   ‍Integrating MLOps creates a cost-effective environment for data scientists to experiment with ML, creating psychological safety. Since they have more time to spend on novel algorithms, they are typically happier.

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.

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More questions on MLOPs?

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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.