Why do we need MLOps

MLOps and AI infrastructures are topics that have been widely discussed in recent months, even more so after the rise of technologies around LLMs like ChatGPT.

In this blog post, we’re going to give a short and gentle introduction to these concepts by introducing their basic aspects.

Let’s start by introducing the practice of MLOps and what it means.

MLOps, or Machine Learning Operations, combines machine learning development activities and DevOps practices.

It considers the entire machine learning lifecycle, from data preparation and experimentation to deployment and monitoring phases. The canonical workflow showing the MLOps process is the following:

MLOps schema

Schema original source: MLOps.org

The first steps are about requirements and data gathering, followed by the development phase (data preparation, feature engineering, training, running experiments…), model packaging (artifact creation, storage in a model repository…), until the final deployment, monitoring, and observability phases.

However, we can think of this process as iterative. If a model becomes obsolete in production due to poor performance or the presence of concept/data drift, the data scientist must update it and go through the various steps again. In this case, if we have a cycle between the monitoring and the training phases, we can talk about Continuous Training or Continual Learning, but we will go deeper into these topics in a future blog post.

What are the key benefits of MLOps?

As in many other industries, the use of well-structured and verified practices brings many benefits. In the AI domains, and specifically when it comes to MLOps practices, there are multiple advantages that are provided, here are the more significant ones:


  • increase productivity – by relying on established practices, the team can be more productive and avoid reinventing the wheel each time without starting from scratch
  • improve collaboration between engineering, DevOps, and data teams – in large organizations, there are typically multiple teams. The data science team is usually focused on defining and writing the algorithms and training the models. On the other hand, the engineering and DevOps teams are more focused on putting the models into production and monitoring them in a perfect way to avoid their obsolescence and poor performance from both a functional and non-functional requirements point of view. MLOps can help to break down silos and make it easier for teams to work together and reach better results
  • improve model quality – by applying standard and recognized practices, it’s an easy task to get better models with great quality. In addition, continuous monitoring and observation of the models keeps them up to date, prevents them from becoming obsolete, and ultimately achieves higher overall quality;
  • reduce costs – following MLOps practices can shorten the development cycle and optimize the amount of hardware and software needed to run the models, ultimately yielding a noticeable reduction in costs
  • faster time to market – the combination of some of the previous benefits (e.g., productivity and collaboration improvements) ultimately leads to a model in production in a fraction of the time
  • regulatory compliance – MLOps can help ensure that models are developed and deployed in a transparent and auditable manner. Moreover, the application of techniques such as explainability and observability is often a regulatory requirement in many domains. Last but not least, MLOps can help to have better control over the model, ensuring appropriate and ethical behavior, and avoiding both bias and hallucinations.

Looking ahead: AI infrastructure & MLOps

AI Infrastructure is the combination of hardware and software needed to develop, train, deploy, serve, monitor, and observe AI models.

Analyzing the hardware side, we need to consider elements such as computing resources (CPU, GPU, or TPU), memory, storage, and networking among each other. Modern models, such as LLMs, require ad hoc and powerful hardware to run correctly.

On the other hand, when we talk about software requirements, we need the best off-the-shelf tools like Model Management, Continuous Integration and Delivery, Model Monitoring & Observability to ensure proper management of models at every stage.
That’s why it’s essential to rely on a modern AI infrastructure to implement and mature MLOps practices in a company.

In short, AI Infrastructure provides the resources and tools that ML teams need to develop and deploy ML models, while MLOps provides the practices and processes that help them to do so more efficiently and reliably.
Clearly, these two concepts are inextricably linked and essential for any organization that wants to truly apply AI at scale.


As we’ve discussed, given the various benefits of applying MLOps practices, it’s a must to implement them in organizations, at least after an initial prototyping phase.
Working with artificial intelligence is a hard and heavy task, and of course, we need to have all the tools in place to gain full control and avoid potentially catastrophic disasters.

If you are interested in knowing more about the Radicalbit MLOps platform, do not hesitate to reach out.


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