Why MLOps should be at the top of your to-do list

If you’re new to the world of MLOps, here’s what you need to know: MLOps (which stands for machine learning operations) is a set of tools and ideas that help data scientists and operations teams to develop, deploy and monitor models in the AI world.

That’s a big deal because organisations that want to deliver AI projects often struggle to get projects off the ground at scale, and to deliver effective return on investment (ROI). Using MLOps helps those organisations to create machine learning models in a manner that is effective, consistent and scalable.

Over the last decade, machine learning has become a critical resource for many organisations. Using ML models, companies can create models that can analyse vast quantities of structured and unstructured data, making predictions about business outcomes that can be used to inform faster, better decisions. The challenge, increasingly, is how those organisations monitor and manage multiple ML models and iterations.

MLOps brings discipline and structure to AI

That’s where MLOps comes in. While DevOps focuses on how systems are developed with regard to security, compliance and IT resource management, MLOps focuses on the consistent development of scalable models. Blending machine learning with traditional devops models creates an MLOps process that streamlines and automates the way that intelligent applications are developed, deployed and updated.

Examples of how MLOps is being used include:

• Telecoms – using MLOps systems to manage network operations and customer churn models.
• Marketing – in advertising, MLOps is being used to manage multiple machine learning models in production to present targeted ads to consumers.
• Manufacturing – Using machine learning models to predict asset maintenance needs and identify performance and quality problems.

With MLOps, Data scientists can place models into production, then monitor and record their performance to ensure they’re working well. With MLOps they can also capture information on all ML models in a standard form that allows other teams to use those models or revise them later.

How MLOps can deliver higher ROI

This isn’t just about making life easier. We know that 90% of AI projects fail under current development frameworks. MLOps provides a far more reliable, cost-effective framework for development that can deliver successful projects much more quickly. By adopting MLOps, it becomes easier for organisations to make the leap from small-scale development to large-scale production environments. By increasing the speed and success of ML models being deployed, MLOps can improve the ROI of AI projects.

It’s also worth considering that models – by their nature – need to change. Once an ML model is created and deployed, it generally won’t continue operating in the same way forever. Models need to be constantly monitored and checked, to ensure they’re delivering the right insights and business benefits. MLOps helps data scientists to make faster inventions when models need to be revised – such as during a global pandemic or supply chain crisis – with changes deployed at a faster rate.

If organisations want to adopt MLOps they must first build the relevant skills within data and operations teams. This includes skills such as full lifecycle tracking and a solid AI infrastructure that enables the rapid iteration of new ML models. These will need to support both main forms of MLOps – predictive (charting future outcomes based on past results) and prescriptive (making recommendations for future decisions).

Need more guidance?

The key thing to understand about MLOps is that it can’t guarantee success, but it will lower the cost of experimentation and failure.

Ensuring you get the best results from MLOps isn’t always easy, and our MLOps Playbook is a good place to start for guidance on how to maximise the ROI and performance of models in your organisation. The playbook outlines the basic principles of effective MLOps, including creating solid data foundations, creating an environment where data scientists can create and the pitfalls to avoid when creating MLOps practices.