In today’s fast-paced digital landscape, organisations are constantly seeking ways to streamline and optimise their software development processes. DevOps has been a game-changer in software engineering, enabling faster development cycles, improved efficiency, and better collaboration between development and operations teams. Now, as machine learning (ML) systems become more integral to business operations, the principles of DevOps are being adapted to ML workflows in what is known as MLOps (Machine Learning Operations). In this blog, we explore how DevOps practices apply to machine learning, the benefits of MLOps, and the steps organisations can take to move toward a fully automated MLOps framework.
What is MLOps?
Also known as Machine Learning Operations, MLOps streamlines the development of machine learning systems by bringing developmental and operational elements together in an automated manner, usually through the application of DevOps principles.
DevOps on the other hand, has been transformative in the way software systems are built, tested, deployed and operated.
What are the potential benefits of DevOps applications?
Companies that have embraced DevOps have seen significant benefits, including:
- Reductions in the time to get products to market
- Faster resolution of technical issues
- Quality and efficiency gains across the software development lifecycle
- Reduced costs
- Higher quality products and services
- Better customer service
- The ability to accommodate change faster
Although there are several significant differences between machine learning systems and more typical software systems, there is enough similarity in the way they are built and operated to apply the DevOps culture to the development of a machine learning system for similar benefits.
How can I move my organisation down the path towards fully automated MLOps?
Understanding where you want to get to and why is key. This will help inform the decision-making process when it comes to tooling and process adoption or change.
Once you’ve established and aligned your goals to your business strategy, you’ll also need skilled engineers. Building automated systems like this requires engineers with a broad skillset covering both development and operations. As data science teams might lack the skills required to build MLOps systems, having DevOps engineers working closely with your data science teams will be vital to the successful delivery of your MLOps system.
As with many other areas of computing, there are maturity models available for assessing your position. Typically, they are defined as a set of levels where progression from one level to the next is dependent on having certain characteristics built into your overall machine learning system.
Adopting a maturity model for your machine learning system can help you to understand where you are on the journey to fully automated MLOps and what the next stage is.
The bottom line
Implementing MLOps can significantly enhance the efficiency, scalability, and reliability of machine learning systems by incorporating DevOps principles. However, achieving fully automated MLOps requires a clear strategy, skilled engineering teams, and a structured approach using maturity models. As businesses continue to embrace AI-driven solutions, adopting MLOps will be key to staying competitive and ensuring long-term success in an increasingly data-driven world.
One differentiator between machine learning and other systems is the need for regular model training. Machine learning models are trained frequently to further improve their efficacy. This is an area where DevOps practices can again have a significant benefit and lead to the MLOps concept of continuous training or CT.
If you want to reach out to our DevOps practitioners to learn more, get in touch on transformation@transformuk.com.