Machine Learning Operations: Benefits and Applications in Financial Services

Date:June 17, 2024

Machine Learning Operations (MLOps) is on the rise, with the predicted global MLOps market estimated to be worth $4 billion by 2025. Designed to overcome the challenges associated with the Machine Learning lifecycle, successful MLOps implementation requires a combination of technical expertise, organisational commitment, and strategic transformation. In this article, we explain the benefits of using MLOps, with a particular focus on Financial Services, and describe how organisations can ensure they have the correct framework in place to unlock the power of Machine Learning and stay ahead of the curve.

What is MLOps?

MLOps is a practice that uses people, processes, and technology to efficiently automate the Machine Learning lifecycle. While DevOps aims to improve processes by bridging the gap between Development and Operations, MLOps streamlines the process of not just model development, but also deployment, monitoring, and systematic retraining. It effectively addresses the unique challenges stemming from data collection, parameter settings, deployment, and the continuous retraining of data that is required for Machine Learning.

MLOps is a practice that uses people, processes, and technology to efficiently automate the Machine Learning lifecycle.

The MLOps process consists of three interconnected components. Firstly the “design” phase, which defines the project and requirements. Next “model development”, which includes engineering, collecting, cleaning, and formatting the data before creating the ML model. Upon deployment of the evaluated model, it is monitored for maintenance, updates, feedback or retraining as necessary, as part of the “operations” stage.  Additionally, review and governance are integral across all components of the model, and the continuous feedback iteratively improves the system over time.

MLOps - Projective Group

Why use MLOps?

Machine Learning helps individuals and businesses unlock value by creating efficient models and workflows and leveraging data analytics for decision making. Without a solid operating framework, productionising ML is a difficult challenge. MLOps plays a crucial role in monitoring alterations in new data, keeping tabs on parameter modifications and facilitating feature engineering.

MLOps optimises the time to market metric for any new model. According to a report by the International Data Corporation (IDC), organisations that adopt DevOps practices that include MLOps experience a 63% reduction in time to market compared to traditional development methods.

Organisations that adopt DevOps practices that include MLOps experience a 63% reduction in time to market.

It is also easier to align models with business requirements, as using MLOps reduces technical restraints and risks, whilst continuous re-training and redeployment ensures better accuracy in the long run.

Without MLOps, organisations may face inefficiencies, security risks, and difficulties improving using manual processes and adapting ML models over time. A study published in the International Journal of Engineering Research & Technology, found that organisations implementing MLOps practices experienced a 35% reduction in operational costs related to machine learning model deployment and maintenance.

Importance in Financial Services

As market dynamics are evolving, the finance industry demands scalable and agile ML solutions. Plus, the lifecycle of these models from an idea to production must be secure, transparent, and aligned with business objectives. With the exponential increase in data, outdated data can produce inaccurate predictions and be harmful to a business, increasing legal and compliance risks in an already high regulated industry. With MLOps, quick production to deployment mitigates these risks and ensures financial models stay relevant, timely and competitive.

The Financial Services industry has a growing number of ML use cases. In particular, MLOps can be leveraged to improve an organisation’s fraud detection capabilities. Transaction data can be continuously monitored, and ML models updated in real-time, to achieve higher accuracy in identifying fraudulent results, while minimising false positives. Other use cases include onboarding and document processing, credit scoring, optimising trading algorithms, and safe payment processing. 

How to get started with MLOps

MLOps focusses on uniting three key areas: people, process, and technology. A good place to start is understanding your organisation’s data maturity and expectations. In addition, acclimating teams to MLOps software practices is vital in providing the necessary support for the technical and organisational structures required in ML operations.

MLOps focusses on uniting three key areas: people, process, and technology.

This emerging practice has a growing number of tools and methodologies to aid its implementation. As an example, we recently helped a client define their MLOps principles and operationalise an MLOps pipeline using the in-built capabilities of Dataiku, with the support of other tools like GitHub. This enabled us to productionalise 30 data products and ML models for this organisation.


Overall, MLOps focusses on the end-to-end lifecycle management of an ML model. It is not just about implementing the model, but also about establishing robust processes, supporting collaboration, and continuously improving and adapting ML-driven products to meet changing business needs.

Beyond MLOps, it is also important to consider other key emerging transformations such as Artificial Intelligence Operations (AIOps). While MLOps tools monitor data for building ML models, AIOps utilises ML to automate the management of applications. Although serving different functions, these tools can be used together to unlock even greater business value.

If you would like to find out more about how Projective Group can help you adopt or enhance MLOps in your organisation, get in touch with us today.

About Projective Group

Established in 2006, Projective Group is a leading Financial Services change specialist.

We are recognised within the industry as a complete solutions provider, partnering with clients in Financial Services to provide resolutions that are both holistic and pragmatic.  We have evolved to become a trusted partner for companies that want to thrive and prosper in an ever-changing Financial Services landscape.