Machine learning operations, MLOps, is the organizational practice for operationalizing AI and accelerating ML development in a sustainable way. Once you’ve validated a couple of AI use cases by pilots and proofs-of-concept, scaling the development and the use of AI in your organization will require both a solid machine learning infrastructure and an aligned way of operating. If you’re not already working on it, it’s about time: companies like Netflix are successful because they are AI-driven and that’s thanks to well-working MLOps. In this article, I’ll outline key learnings we’ve had at Silo AI, building these necessary structures together with our clients to ensure they’ll improve throughout their AI development pipeline.
Put people at the core of developing AI with MLOps
Creating solid MLOps is key in organizing your business and, above all, your people around data, ML and AI in a sustainable way. MLOps is building on top of the DevOps way of working, which might be familiar from software development.
DevOps aims at reducing and removing the disconnects between people and teams so that technical development matches the various business and other needs better. One element in this is to bring together the business, development and operation functions of organizations. In addition, MLOps also brings together machine learning development and traditional software development which have some fundamental differences between their workflows.
Good MLOps addresses both typical DevOps aspects and a number of special factors related specifically to the development and utilization of AI and ML. In addition, MLOps helps people in developing several ML models at the same time through assistive automation, which supports scalable data utilization, model development and AI deployments. The technical part of MLOps – the platform and the assistive automation therein – essentially helps all the team to take ownership and responsibility of delivering ML and AI results.
Achieve continuous delivery of AI features
Companies that are already strong in their AI development and MLOps are both strategically aligned in their vision, roadmaps and their organization as well as have allocated sufficient resources to the process. The ownership and resourcing should be sufficiently cross-organizational as AI and ML have even more collaboration needs than the traditional solution and product development and delivery.
Sufficient MLOps practices allow companies to transition from having individual AI and ML exercises, or infrequent and hard-to-maintain releases, to continuous delivery of AI features. MLOps ensures that organizations constantly improve their AI features with new collected data, create more and more business value from those features and from additional data sources, such as their customer base.
Match the increasing needs for expertise by organizing the work smartly
MLOps provides a way to organize your limited resources in the most value-creating way. Given the increasing need for AI and ML expertise, MLOps provides a way to easily combine in-house teams with outside specialists. Acquiring sufficient AI and ML skills can be hard, especially concerning senior roles. Successful AI-driven organizations have enough in-house resourcing and initial competence to get started and progress but they are also flexible enough to scale their activities through external collaboration.
Without solid MLOps infrastructure and ML operations in place, it will be challenging to progress with AI – the scarce skills are wasted on repeatedly restarting activities from the beginning and doing trivial tasks and maintenance, instead of scaling and expanding AI features for versatile use cases around the business.
Choose your battles to stay ahead of your competition
AI and ML can yield a unique value that can’t be reached through previously available means. The current and future applications of computer vision (CV) and natural language processing (NLP) as well as the capability for customized data utilization, detection and prediction, would not be possible through traditional software and systems development. Your competitors are likely thinking about the same thing as you are: it’s time to stop one-offs and focus.
However, ML development and AI utilization also require a lot of re-organizing, due to the additional complexity that they entail. The key to success with AI is choosing the battles and focusing the resources on the most value-adding tasks. These most productive tasks are often also the most innovative – meaning they require a lot but give a lot too.
Having a solid ML development environment and infrastructure – including human processes and the technical platform – directs the work from one-off exercises into continuous delivery of AI capabilities. The same MLOps framework can be used for turning business problems into developing AI features but also for testing them in practice.
As I’ve seen in my work, many companies have already spent a lot of time learning and exploring AI and machine learning. I’d say we’ve now arrived to a certain maturity to adopt and use AI in a scalable way. If you don’t want to miss the window of opportunity, you should now start operationalizing AI as a key part of your products and solutions, instead of treating AI and ML as isolated development or purely research. Having proper MLOps is central in becoming truly able to utilize AI and in growing into an AI-driven organization.