What is Machine Learning Operations (MLOps) and why do you need it?


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MLOps (Machine Learning Operations) is the set of practices, processes and principles that help us in deploying, running and maintaining machine learning algorithms at scale in live production environments.

Victor PereboomCTO at UbiOps
MLOps: MlOps Workflow UbiOps

What is MLOps?

From decades of experience in developing and managing complex software products, many DevOps best practices and processes originated. In the end, data science and machine learning is also software, but with a few quirks that make it more difficult to manage than regular software products.

The main reason being that its performance and behavior not only relies on its source code, but also on the data used to train it and the data that flows through it.

This means that there are different moving parts that need to be controlled and maintained: The source code, the model and related artifacts, and the data. The complexity that ensues from this asks for a new set of DevOps processes and tools, specifically for managing Machine Learning products. This set of processes and tools is what we call MLOps.

At the core of MLOps are Continuous Integration, Continuous Deployment (CI/CD) and the new Continuous Training augmented with elements such as testing, monitoring and version control of code, models and data.

MLOps is not limited to machine learning, as many parts of it are applicable for the entire realm of data science and analytics applications.

The most important elements of MLOps


Experimentation & Training

The iterative process of experimenting with different models, features and parameters to develop the right algorithm for the business case. Not really part of MLOps, but it is important to keep track of which datasets and features are used to produce the outcomes.

Deployment (CI/CD)

The model or pipeline is packaged and pushed to a serving environment. Often a model registry with version control is included with metadata of the source control from data, code and model artifacts.

Testing & Validation

This could be seen as part of the deployment step, but it involves validation of the model performance on live data before promoting it to production. This testing step of ML software is more complex because the combination of code and data defines the performance.

Model Serving

Running the model live on incoming data. Often this takes the form of a prediction (micro)service which is exposed to the environment for processing data. Tooling needed for scaling and orchestration of multiple models simultaneously can get quite advanced.


Continuously keeping track of changes in model performance and data distribution to ensure the model is performing according to its initial benchmark. This could also include metrics around explainability of ML models.

Maintenance & Re-training

Models can degrade over time. Therefore, when performance becomes lower it might be necessary to retrain the model on new data. This can be done manually (going back to step 1), or automatically by executing a (re-)training pipeline.

Why MLOps is important for your team

Whether you are a start-up developing a new AI driven product, or part of a data science team in large company, at some point you need to ship your data driven solution to production. It needs to run live, meet availability standards and be manageable as part of a larger system.


For many teams, this is easier said than done and often requires both knowledge of the algorithm, as well as of IT infrastructure and software. MLOps can be seen as a set of processes and tools to unify the integration, delivery and maintenance cycle of data driven products, making data science and DevOps teams ship products together in a very agile and effective way.

For data science teams, it means that their time can be spent on experimentation and development, instead of IT work and maintenance. For IT and DevOps teams, it means that data science code can be managed much easier with less chance of errors.

For companies in general, it is a way to faster achieve your AI/ML goals and ship your ground breaking data-driven products.

Also, in an MLOps process and architecture, safeguards for security, governance and compliance can be built in to make secure and resilient data science applications for use in the most demanding environments.

How to choose the right MLOps infrastructure for your team or company?

There is a lot to consider when designing or choosing an MLOps infrastructure and it might be overwhelming at first. Each team can have different needs which calls for a standard stack of interoperable niche tools. It’s also good to keep in mind that no business case is the same and emphasis can be on different aspects of the deployment cycle. Maybe it’s more important for your application to run fast than to be always available? Or maybe the infrastructure doesn’t have to be transparent as long as automated deployments are offered?


The MLOps space will see a lot of development and growth over the next few years as the reliable operation of ML within organizations becomes critical for delivering the expected return on investment for advanced analytics.

UbiOps enables you to go quickly and easy from code to live application.

What does UbiOps offer around MLOps?

UbiOps is a serving platform to run data processing code and algorithms in production, at scale. We have built UbiOps to help you accelerate the use of data science in your company. With UbiOps you can create live web services from your ML & AI models and let the platform take care of automatic scaling, security and uptime.


UbiOps can fulfill an important role in your MLOps infrastructure as a serving tool, model registry and central place to manage and maintain your deployed models and algorithms. It helps you and your team deploy and run both simple data processing functions as well as complex ML pipelines.


We also offer many templates for integrations with other parts of your stack: Databases, dashboards, monitoring & explainability tooling, and much more.

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