What is MLOps?
MLOps ensures machine learning models are properly integrated with existing infrastructure, a data scientist checks the model serving infrastructure and monitoring tools to ensure reliable and scalable deployment, and that the model continues to perform as expected over time, which is critical for business outcomes.
A data scientist at a large retail organisation is tasked with deploying a machine learning model to predict customer churn, and they need to ensure the model is properly integrated with the existing infrastructure, which is where MLOps comes in, a term that refers to the operational practice around models in production, encompassing the processes and tools used to deploy, monitor, and maintain machine learning models in a production environment.
Definition
MLOps is a critical component of the machine learning lifecycle, as it bridges the gap between model development and deployment, ensuring that models are properly tested, validated, and deployed to production, and that they continue to perform as expected over time. This involves a range of activities, including model serving, monitoring, and updating, as well as ensuring that the model is properly integrated with other systems and tools.
The goal of MLOps is to ensure that machine learning models are deployed in a way that is reliable, scalable, and maintainable, and that they continue to deliver value to the organisation over time. This requires a range of skills and expertise, including data science, software engineering, and operations, and involves close collaboration between different teams and stakeholders.
Difference from DevOps
MLOps is often confused with DevOps, but the two are distinct, although related, disciplines. DevOps is a set of practices that aims to improve the speed and quality of software development and deployment, by bridging the gap between development and operations teams. MLOps, on the other hand, is specifically focused on the operational practice around models in production, and involves a range of activities that are unique to machine learning, such as model serving and monitoring.
While DevOps is primarily concerned with the deployment of software code, MLOps is concerned with the deployment of machine learning models, which requires a different set of tools and techniques. For example, machine learning models require specialized serving infrastructure, such as model servers, and require ongoing monitoring and maintenance to ensure that they continue to perform as expected.
Applicability in a large organisation
In a large organisation, MLOps is particularly important, as it enables the deployment of machine learning models at scale, and ensures that they are properly integrated with other systems and tools. This is critical, as machine learning models can have a significant impact on business outcomes, and can be used to drive a range of applications, from customer churn prediction to recommender systems.
A shift supervisor at a manufacturing plant, for example, may use a machine learning model to predict equipment failures, and MLOps ensures that this model is properly deployed and maintained, and that it continues to deliver value to the organisation over time. Similarly, a category manager may use a machine learning model to optimise pricing and inventory, and MLOps ensures that this model is properly integrated with other systems and tools, such as the organisation's ERP system.
Limitations
While MLOps is a critical component of the machine learning lifecycle, it is not a silver bullet, and has a number of limitations. For example, MLOps does not address the issue of model interpretability, which is critical in many applications, such as healthcare and finance. Additionally, MLOps does not provide a guarantee of model performance, and requires ongoing monitoring and maintenance to ensure that models continue to deliver value to the organisation.
Furthermore, MLOps requires significant expertise and resources, and can be challenging to implement, particularly in large and complex organisations. For example, a large organisation may have multiple machine learning models in production, each with its own unique requirements and dependencies, and MLOps requires a range of tools and techniques to manage these models and ensure that they continue to perform as expected.
When to choose an alternative
In some cases, MLOps may not be the best choice, and an alternative approach may be more suitable. For example, in cases where the organisation has limited resources and expertise, or where the machine learning model is relatively simple and does not require ongoing monitoring and maintenance, a more straightforward approach may be more suitable. In these cases, the organisation may want to consider alternative approaches, such as partnering with a vendor or seeking external expertise, and can contact us to discuss their options in more detail.
Frequently asked questions
What is the primary goal of MLOps in a large organisation
The primary goal of MLOps is to ensure machine learning models are deployed in a way that is reliable, scalable, and maintainable.
How does MLOps differ from DevOps
MLOps is specifically focused on the operational practice around models in production, involving unique activities such as model serving and monitoring.
What are the limitations of MLOps
MLOps does not address model interpretability and requires ongoing monitoring and maintenance to ensure model performance.
When should an organisation consider an alternative to MLOps
An organisation should consider an alternative to MLOps when they have limited resources and expertise or when the machine learning model is relatively simple.