ML scalability is scaling ML models to handle massive data sets and perform many computations in a cost-effective and time-saving way.
What is Scalability?
Scalability refers to a system’s ability to handle increased or decreased load such that it responds swiftly to changes in applications and system processing requirements.
Machine learning scalability refers to scaling ML applications that can handle any amount of data and perform many computations in a cost-effective and time-saving way to instantly serve millions of users residing at global locations.
ML scalability is achieved by combining Statistics, ML, and Data Mining into flexible, scalable, and often nonparametric techniques. It offers multifold benefits to the organization, such as scaled productivity, better automation, enhanced modularization, and cost-effectiveness.
ML scalability with an example:
A model built to predict stock prices consumes data from a large dataset and delivers prediction instantly. These predictions are relevant for a limited timeframe, and delayed predictions become meaningless from the user’s perspective. Stock prices are super dynamic by nature and hence getting an instant stock prediction is very important here. Scalability comes as a rescue in such situations. It allows scaling ML models to serve millions of users and fits well for big data.
Challenges in Achieving ML Scalability
ML model training is expensive and challenged by data complexities. It is not just getting million relevant records to train but also ensuring data feasibility and predictability for better performance. A robust governance system and data cataloging drive transparency to ML systems.
ML system engineering
A scalable ML system needs to be engineered by considering specific use case requirements and future resilience. Choosing a suitable infrastructure and technical stack is crucial to ML scaling success. The toolkit that translates well into a production environment simplifies scaling ML projects. Inappropriately designed and engineered ML solutions incur more cost with additional complexities in scaling and stabilizing ML models in production.
Integration is hard
Scaling ML project requires a scalable production environment well integrated with modeling technologies and different datasets. It also demands proper integration between various teams and operational systems. Workflow automation, process standardization, and thorough testing practices help build sustainable ML systems that provide value even if the business environment changes considerably.
ML scaling requires seamless and transparent communication across engineering, ML, DevOps, and other teams. Delegating roles, responsibilities, and permissions, as well as monitoring the team performance, is challenging.
A Way towards ML Scalability
Machine learning scalability is not easy. It requires a sound action plan to answer these types of questions:
- How to scale ML systems - horizontal scaling or vertical scaling?
- If autoscaling is the chosen strategy, which is better - scheduled scaling or dynamic scaling?
- How does caching help ML scaling?
- Is there any automated monitoring system in place? Is scaling ML simplified with monitor alerts?
- How distributed data processing frameworks streamline scaling initiatives?
ML scaling requires collaborative team efforts and upfront assignment of responsibilities and roles. Although challenging to achieve, ML scalability is the future with unparalleled benefits – automated and cost-effective ML experiments, better productivity, and reusability.
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