Table of Contents
While it’s always wise to kick off AI adoption with a small-scale use case first, your solution will be useless if it can’t scale properly. Overlooking scalability during AI development can be costly as it can lead to performance issues, business disruptions, and out-of-control costs.
Here are the five ways to ensure your AI/ML solution is built to scale as your business needs evolve.
Set Up a Scalable Data Management Framework
Upholding the pre-defined data quality standards is a major challenge in scaling AI systems. To ensure the data can flow efficiently to and from your AI solution regardless of its volumes, you need to automate as much data processing as possible. Automation will also ensure raw data can be efficiently prepared even when the number of requests rises.
Centralizing all raw data in a data lake, data warehouse, or similar repository that serves as a single source of truth ensures data quality, consistency, and efficient integration. Implement a data lifecycle management strategy to keep your data relevant and up to date.
On top of that, you should also pay close attention to scalability when you set up your data pipelines. For example, your data lake solution may cap data volumes based on your subscription plan.
So, ensure every layer and component can support increased data volumes while remaining cost-efficient.
Pay Attention to Your Cloud Infrastructure
AI solutions are notoriously resource-intensive, and the more data they have to process, the more computing power they require. That’s why AI solutions are deployed to the cloud: this type of infrastructure allows for adding more computing capacity instantaneously.
To ensure your cloud infrastructure remains cost-effective while supporting fluctuations in load:
- Identify and eliminate or mitigate scalability bottlenecks in your infrastructure
- Set up automated scaling for storage and computing resources to right-size consumption
- Minimize integration complexity to ensure smooth interoperability
Make the Right Architectural Choices
Whether you hire an external AI developer or build your AI solution in-house, make sure the development team designs the solution’s architecture to be scalable. In practice, that means:
- Ensuring the AI model can support diverse data types from pre-defined and new sources
- Following a containerization and microservices approach for long-term architecture agility
- Implementing parallel processing techniques and distributed computing support to maintain stable performance under high workloads
- Optimizing the model using model pruning techniques
- Using partitioning and sharding for large datasets and caching for frequently used data
- Integrating hardware acceleration support for managing large computational loads in complex models
Automate AI Operations
Besides ensuring your AI solution can handle spikes in data volumes or requests, you also need to streamline developing, monitoring, and updating the AI system itself. This is where the MLOps (machine learning operations) methodology comes in.
MLOps is a set of practices used in AI development projects to automate and standardize processes, speeding up time-to-market and increasing flexibility as a result. Adopting the MLOps approach means implementing:
- Version control for ML assets to ensure reproducibility and auditability
- Automation across the ML pipeline, from data ingestion to model testing, validation, and deployment
- Continuous integration, delivery, training, and monitoring
- AI model governance practices that prioritize cross-functional collaboration, clear documentation, full alignment and security, and responsible AI development
Continue Monitoring AI Model Performance
Once your AI solution is deployed, your team should keep an eye on its performance metrics, such as accuracy, recall, error rates, resource usage, etc. These metrics will inform your decisions regarding fine-tuning and adjusting the AI model overall – and signal when scalability issues arise before they can cause substantial damage.
Tracking these metrics is also useful because it enables you to set up automated scaling. For example, whenever the solution passes a specified CPU or memory usage threshold, a new server can be added to your capacities to meet the demand.
In Conclusion
In addition to the ways to ensure scalability listed above, you should also consider your technical debt across the totality of your digital ecosystem. If left unaddressed, your legacy systems’ lack of support for scaling may undermine your effort to build a scalable AI solution.
If you need an AI expert who considers solution scalability from day one, reach out to S-PRO – an AI development and data science partner with over a decade of experience.