In my first blog, we introduced the concept of MLOps (Machine Learning Operations). Now, we will go through the benefits of applying MLOps – which are considerable.      

Using MLOps to unlock much faster ML project delivery

MLOps reduces overall project times in several ways:

  • Handovers are quicker. Having a documented process makes it easier for everyone to know their role; from the data scientist handing off the model to the software team, to the data engineer updating the data pipeline. MLOps speeds up handovers across the whole process, including discovery piloting, deploying, monitoring and support.

  • Issue resolution is quicker. Reproducing a bug or issue in the code, model or data is simplified, because the team can audit back on the correct version and state. When you can reproduce an issue quickly, then rectifying it is much easier.

  • Starting a new project is quicker. MLOps streamlines through a standardised process, providing the building blocks for success. Reuse of assets from prior projects helps to save time, while also supporting good practice. 

Championing repeatability to make ML more explainable, auditable and compliant

An ML model and its output needs to be understood and explainable if it is to be relied upon. This requires being able to audit back into the original experimentation process. For example, if a customer or user needs an explanation for a specific decision.

In some industries (healthcare or financial services) that level of audit and explainability may be a formal regulatory requirement. And don’t forget internal stakeholders, such as business owners, who need to understand the model and how it impacts and benefits their product or service.  

 Transparency and MLOps go hand-in-hand. A standardised and repeatable process is naturally more transparent. There are clear steps, participants and roles and responsibilities, while every key step is traced and auditable. 

Do you want better performing “all round” ML models?

MLOps delivers better all-round ML models. Models that operate correctly, robustly and are stable. By harnessing the cloud, MLOps can deliver data science at scale, underpinned by highly performant models.

The ML model and associated data pipeline may only be one component part of an overall product or service delivery platform. Here, MLOps helps through testing and monitoring to ensure the veracity of outputs. This capability helps data scientists to adjust to check that core and edge ML requirements are supported or flag up the gaps or model drift.

This application of continuous integration and deployment to ML leads to better performing all round models, as the models are optimised over time. Over time, as the organisation standardises more, this will also have an impact on making the models easier to support and maintain.

MLOps key enabler to cross-organisation collaboration

Data scientists are very talented people, but they may not necessarily understand the live environment or the exact business context. The MLOps process ensures these factors are taken into account, enabling cross-collaboration with DevOps, engineering teams and line of business owners as needed, with the MLOps process ensuring key stakeholders’ requirements are assessed correctly. 

The more you do it, the more you gain!

Speed, transparency, and better all-round ML models are the key benefits of MLOps. There is also a more holistic benefit; a switch in mindset for ML projects, where the MLOps process is focused on going live with ongoing updates and optimisations. As you scale and run more and more ML models live, the AI/ML story changes.  The thousands of potential ML use cases become more real and more tangible. MLOps supports scaling up ML activity and will also instil confidence into organisations that they can reap the potential ML benefits. That confidence is self-fulfilling.

You could work for an insurer trying to detect fraudulent claims; a bank looking to improve the customer onboarding process; or a manufacturer using predictive maintenance to reduce plant and equipment costs. You may work for a retailer or travel operator looking to forecast demand, optimise price or assess a shopper’s propensity to buy. Or part of a government or healthcare organisation looking to provide better patient and citizen care by joining up systems and digitising paper records. Whatever industry you are in, MLOps will enable you to apply AI in a better, faster, cheaper way.  

Tune in next week…

Hopefully, this has given you an outline of the benefit of MLOps. In my next blog, we will discuss the challenges of embedding MLOps into an organisation and how you can overcome them. 

Are you interested in finding out how we have operationalised Data and AI solutions for our customers? Check out our Machine Learning Operations (MLOps) services page to read our latest case study or speak to an expert.