Machine learning operations (MLOps) is a newly emerging best practice in the enterprise space that is helping data science leaders effectively develop, deploy, and monitor data models. A compound of machine learning (ML) and operations, the market for MLOps solutions is predicted to grow from US$350 million in 2019 to reach almost US$4 billion by 2025. With such rapid growth ahead, it's crucial that businesses prioritise MLOps innovation now.
Similar to how DevOps emerged from the need to provide a framework for the software development lifecycle, MLOps was developed as a framework for the development of ML systems.
ML development and deployment comprises a complex set of people, processes, and technologies with a lifecycle that needs to be managed, monitored, and optimised to be effective.
Now that businesses have recognised the value of AI and ML, it is important they now focus on extracting the promised value from those ML systems through MLOps.
MLOps in the enterprise space is showing no signs of slowing down. Here are four ways companies can start testing AI more effectively and efficiently:
Focus on model deployment
ML mathematical models have a lifecycle 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.
Prioritise model security and governance
Attacks against AI and ML models continue to be conducted by bad actors and exposed by leaders in the research community. As MLOps grows in prominence within the IT industry, professionals must 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.
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 decaying or improving over time.
For example, a model that executes quickly on small amounts of data might find itself struggling with many 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.
Automate to scale
Automation through MLOps is critical to scale ML-based production systems. As AI becomes more and more democratised and essential to businesses of all sizes, MLOps will become a crucial requirement for the 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.
Currently, MLOps tools are dramatically impacting 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.
