MLOps is an exciting and relatively new concept in the world of AI. MLOps, or Machine Learning Operations, is a methodology that details how to effectively develop, deploy, and monitor models in a structured and segmented manner. In real-world ML applications, the emphasis is on the smooth development of models, and on their scalability.
Having recently moved from the proprietary world of SAS (although they’re moving in the right direction) into the Open Source heavy MLOps environment the first thing I wanted to work out was, “What is it really going to deliver for our clients?”
Many businesses such as Capital One, Covea, and Noitso have made significant investments in Machine Learning that has resulted in teams of people building, training, deploying and managing the ML model lifecycle. It is this manually intensive effort that the MLOps platform is there to mitigate.
Time to Value
The true value is in speeding time to market which ultimately means time to the revenue uplift or cost reduction that the ML model is aiming to facilitate. In our own research, we found that only 13% of models ever make it to production. I’ve had personal conversations with leaders of Data Science teams that bare out the fact that organisations need help with deployment, I’m not sure I ever thought the figure would be that low however!
Seldon Deploy, our Enterprise MLOps platform is focused on the deployment and monitoring of models into a Kubernetes based infrastructure, it’s aim is simply to help organisations raise this figure and get more models into production in a managed and automated fashion.
Better Cross-Team Collaboration
Bottlenecks can often happen when trying to collaborate between operations and data teams. Enabling the Data Science team to iterate models more quickly and ML engineers to manage more models to production deployment through automation and re-useable configurations enables robust scalability and drives cost efficiencies. Developing and deploying models more quickly with less effort means less time and man hours, a direct cost saving to the business.
Saving costs is ideal, but it’s also important to make sure your team is happy with their working environments. After all, a company’s strongest asset is the team doing the work behind the scenes. Optimised workflows result in better collaboration across the entire organisation, from the data scientists, to the machine learning engineers, and DevOps engineers.
Another issue that we recently hosted a webinar on is trust. The webinar was titled, How can Financial Services trust AI? and is available to watch on-demand. AI has faced issues with trust, especially in highly regulated industries such as financial services. MLOps helps to achieve compliance with regulations, and stay up-to-date with shifting regulations that might continually change their requirements.
Build vs. Buy
There is still the option to Build vs. Buy, you could easily build an MLOps platform on top of the Seldon Core deployment engine, and that is a conversation that we have regularly. What’s the additional benefit of buying the Seldon Deploy platform, or any other for that matter? My simplistic and personal view on this is about focusing your efforts on your core business and taking advantage of software vendors for whom this is theirs. Why take on the burden of integration, improvement and ongoing support of a software platform if that isn’t what drives revenue for your organisation?