Machine learning operations (MLOps), a newly-emerging practice in the space enterprise, is helping data science leaders effectively develop, deploy, and monitor data models, according to Micro Focus.
The software company predicts that compound machine learning (ML) and operations, and the market for MLOps solutions will grow from US$350 million in 2019 to reach almost US$4 billion by 2025.
Micro Focus suggests "that businesses should prioritise MLOps innovation.”
Matthew Bertram, Business Consultant, enterprise DevOps and hybrid IT management, Micro Focus, says: “Similar to how DevOps emerged from the need to provide a framework for the software development lifecycle, MLOps has been developed as a framework and best practice for the development and implementation of ML systems.”
Micro Focus says “ML development and deployment comprises a complex set of people, processes, and technologies that have a life cycle that needs to be managed, monitored, and optimised in order to be effective.”
“As businesses have recognised the value of artificial intelligence (AI) and ML, it is important they focus on extracting the promised value from those ML systems through MLOps”, Bertram says.
MLOPs in the space enterprise shows “no signs of slowing down”, claims Micro Focus. It has identified four ways companies can start testing AI more effectively and efficiently:
1. Focus on model deployment
ML mathematical models have a “life cycle that spans from hypothesis to testing, learning, coding, staging, and production. The entire end-to-end deployment process needs to be tracked, monitored, and automated.”
These mathematical models” need to be tested and reproduced on new datasets that were not present during the initial development to detect model drift. This is when the conditions or assumptions of the original model no longer apply. Similar to source code and regression tests for software, models need to be version controlled and automatically, continuously tested.”
2. Prioritise model security and governance
Attacks against AI and ML models are conducted by leaders and are bared in the research community. As MLOps grows in prominence within the IT industry, it’s important that professionals incorporate security into the entire AI lifecycle. Given ML’s dependency on data, data privacy and ethical considerations must be evaluated and considered frequently. Many AI attacks rely on vulnerabilities that can be easily prevented through regular security reviews and testing.
3. Monitor model performance
It is crucial to monitor the model performance in production because ML is rarely binary and is associated with predictive accuracy. Businesses should continuously question how precise the ML model is performing in production on actual data. IT professionals should also measure if performance is regressing or improving over time.
For example, a model that executes quickly on small amounts of data might find itself struggling with a large number of data points in production or new data conditions may impact the computational load. It is important to have monitoring systems that measure and record improved model performance and scalability.
4. Automate to scale
Automation through MLOps is critical to scale ML-based production systems. As AI becomes more and more democratised and important to businesses, MLOps will become a critical requirement for mass deployment and management of those AI systems.
During the initial stages of model development, many of the tasks mentioned above are performed by human data scientists or data engineers, using manual tooling and processes. While this is acceptable during the initial exploratory development phase, over-reliance on human and manual methods will soon unnecessarily limit production, especially as the number of models grows to the hundreds, or thousands.
Bertram concludes: “MLOps tools impact the IT world, helping increase productivity through automation and intelligence. Decision makers and IT leaders must consider the role MLOps will play in their business and recognise model performance, security, and scalability as MLOps continues to evolve and grow in the market.”