In automated machine learning (autoML) models, accuracy and resilience are both essential qualities. A model must be accurate to fit the available data correctly. And it needs to be resilient to adapt to changing circumstances, retain its predictive accuracy over time, and provide the most value for the businesses that use it in their forecasting.
The Importance of Building Resilient Models
Why is building a resilient machine learning model so essential? Before answering this question, let’s first discuss the difference between accurate and resilient autoML models.
Accuracy is crucial in artificial intelligence models. An accurate model will perform highly reliably over the limited test dataset and give a necessary indication of how the model will perform in the real world. However, it may not have broad applicability or perform as well over time.
One reason for the limited performance of a precise model is overfitting. If you use the same few datasets repeatedly and tailor your model so it performs highly accurately with them, you’re not necessarily making it more accurate and reliable overall. You’re merely making it a specific fit for the datasets you have. A model subject to overfitting will be exceptionally exact in testing, but the testing may not tell you everything you need to know about the model’s eventual real-world performance.
Resilience is vital in a model because it provides valuable flexibility and generalizable information about performance. A resilient model is not necessarily the most accurate, or the one that will give you the best area under the curve. However, a resilient model will offer a range of benefits a merely accurate model may not be able to provide.
- Broad applicability: A resilient model will perform well over an extensive array of datasets — not just the test set. When it comes time for deploying artificial intelligence models in the real world, a resilient model will be better able to handle a wider range of data.
- Extended usefulness: A resilient model will also perform well over a relatively long time. While an accurate model may be overfitted, a resilient model is not likely to be. So it will be more robust than other models.
- Less monitoring and retraining: A model that remains useful over a long period is beneficial because it will require less monitoring and retraining. You’ll be free to spend your time on other aspects of the project.
- Sustained business impact: The practical implications of resilient models involve their business impact. You want your models to provide consistent, dependable results in the real world. A resilient model will provide that value for a business.
How to Build Resilient Models
Below are a few tips for building resilient models.
- Focus on simplicity: For greater resilience, use a linear or comparably simple model. Because straightforward models are less likely to be susceptible to overfitting, they are more likely to be resilient than highly accurate.
- Build in structure: To increase resilience, you can often build structure into the underlying platform. A structured or semi-structured platform will provide the centralization, order, alignment, and security necessary for reliability while still providing freedom and agility in the model.
- Incorporate dynamism: Building some degree of flexibility and dynamism into your models helps increase resilience. Prioritizing self-learning and sensitivity and making it easy to implement human corrections can make your models more resilient and dependable. You might use autoML tables models, for instance, to train an autoML model to make predictions on new data by using existing, structured data.
- Use complex models for comparison: Over time, you can run additional, more complicated models, compare them to your initial model, and make tweaks based on your observations.
- Invest in tracking and management: Engaging in post-model monitoring and management enables you to refine your models’ resiliency.
And how can you evaluate the resilience of your model once it’s ready? Though no one standard will measure resilience for you, here are a few indicators to keep in mind.
- Consistent error rates: If you’re optimizing resilience over accuracy, you don’t want your error rates to fluctuate much over time. In production, a resilient model will show more consistent error rates over longer periods.
- Smaller error rate discrepancies: If you see smaller differences between validation and test evaluation sets’ error rates, the model is likely a resilient one. If you see significant volatility, the model may not be as resilient.
- Smaller standard deviations: In a cross-validation run, smaller standard deviations generally indicate a more resilient model.
- Resistance to input drift: A resilient model should also exhibit less impact from input drift.
- More consistent real-world performance: Once your model is out in the world, you should gather data about how it performs. Evaluating the model’s abilities over time will give you the best indicators of its resilience.
Code That Learns
Part of building a self-learning, resilient model involves code that learns. For their machine learning capabilities, many autoML models incorporate neural architecture search. This method relies on a design that automatically builds neural networks out of complicated layers such as batch-norm, convolutions, and dropout.
Some autoML learning models rely on searching for whole algorithms from scratch. This method is more challenging because it requires looking through massive search spaces containing very few accurate algorithms. It provides unique benefits, though, because it enables the discovery of novel and superior ML architecture.
Early research on this topic showed promise, but it had not advanced much over the past two decades, until recently. More current research, though, suggests the possibility of evolving learning algorithms from scratch. Developing brand-new algorithms likely means searching the space of algorithms using variations on classical evolutionary methods, which are ideal in their simplicity and scalability. One process, the autoML zero approach, begins with empty programs. It then goes through repeating cycles, evolving different and more complex algorithms each time.
Contact RazorThink for AI Solutions
To gain resilient, dependable AI solutions for your business, work with Razorthink. Our RZT aiOS operating system provides prebuilt and pretested code blocks you can combine immediately into higher-level AI applications.
This approach allows your company to bypass initial coding work, focus its energy on higher-level AI processes, and add more value to business endeavors. And since an estimated 64 to 84% of a data scientist’s time goes toward developing a model and testing and tuning code, it also dramatically shortens your development time. With advanced features like a powerful engine, a full integrated development environment, and a sophisticated Python software development kit, the RZT aiOS operating system will enable you to build and fine-tune your AI solution in a matter of days or weeks instead of several months.