A majority of artificial intelligence projects are expected to shift from pilot to operational in the next two years, as organizations look to put their multi-year investments into AI to work.
Automating machine learning development and IT operations is the next step for organizations investing time into ML projects, as it can accelerate research and application development, while also enabling data scientists and engineers to run ML models more efficiently and actively.
MLOps is still a nascent field, a branch of DevOps which most technology companies deploy in some fashion. Even though both use the same compound, there are a few differences between the two. In an ML project, most of the team are non-technical, in comparison to DevOps which is primarily software engineers. Machine learning is more experimental than regular software development, and requires more testing and a multi-step pipeline for retraining the model.
Before an organization can transition to automated MLOps, they need to ensure that the data pipelines feeding the machine learning model and other connected applications are automated.
See also: The Evolving Landscape of Data Pipeline Technologies
Automated data pipelines are essential for machine learning operations, as the amount of data collected and analyzed exceeds most other IT operations. With an automated data pipeline, organizations can automate any part of the ETL (extract, transform, load) process, and some providers offer no-code solutions to allow non-technical staff, which ML researchers tend to be, an ability to automate workflows.
With the automated data pipelines in place, organizations can then negotiate automating MLOps. As Google says in its guide to automation pipelines in machine learning, only a small minority of elements in a machine learning system comprise ML code. Most elements have to do with data collection, verification, monitoring, testing, and debugging, which are all part of a continuous delivery system.
By automating a portion of the development and operations process, data scientists and researchers can increase the speed of updates to ML models. Live ML projects, which ingest data in real-time to constantly improve the accuracy of the model, are able to iterate on the fly without much, if any, human oversight.
