How Often Should You Retrain Machine Learning Models?

Retrain Smarter, Not Harder

A common question in the domain of AI and machine learning is: how often should you retrain machine learning models? The answer isn’t as straightforward as you might think. It’s not a one-size-fits-all solution, but rather a process that requires careful consideration and strategic planning. In this article, we’ll explore three strategies for deciding when to retrain your machine-learning models.

Why Retrain Machine Learning Models?

Before we delve into the strategies, let’s first understand why retraining is necessary. Machine learning models don’t degrade or rot over time, but they do need to be retrained to keep improving. As you gather more data and feedback, you need to retrain your model to gain more experience and improve its performance. Even if you don’t see any changes or improvements in performance, retraining is still beneficial as it keeps your model’s knowledge up-to-date, especially in a domain that changes over time.

Strategy 1: Never Retrain Your Models

The first strategy is the simplest one: never retrain your models. This might sound surprising, but many businesses adopt this approach. They collect data, train a model, and then never retrain it. This strategy is advantageous because it’s simple and doesn’t require any support for training infrastructure. However, the downside is that if your target environment is dynamic and constantly changing, your model won’t be able to adapt to these new realities and will start to degrade over time.

Strategy 2: Retrain at a Fixed Frequency

The second strategy involves retraining machine learning models at a fixed frequency, such as daily, weekly, monthly, or yearly. This strategy is a good trade-off between simplicity and keeping your model up-to-date. However, the downside is that you might be retraining your model without any actual need, which can get expensive, especially if you’re using cloud-based or external infrastructure.

Strategy 3: Retrain Dynamically

The third strategy is to retrain machine learning models dynamically, as its performance gets compromised. This means you only retrain when it’s needed. This strategy can save resources, but it’s also the most complex to implement and can be operationally cumbersome. It’s also subjective to determine if the model has actually degraded or not.

So, What’s the Best Strategy?

The best strategy depends on your specific needs and circumstances. If you’re just starting to embrace AI, the third strategy might be overkill. Instead, you might want to consider the first or second strategy.

One approach is to align your retraining schedule with the seasonal period of your business. Alternatively, you could conduct a simulation to determine the optimal retraining frequency. Start by simulating what would happen if you retrained your model at a very high frequency, then gradually decrease the frequency until you see the performance start to degrade. This point is where your model starts to get outdated, and it’s a good trade-off between the cost of retraining your model and the profit you’re losing due to performance degradation.


The answer to “How often should you retrain machine learning models” depends on various factors, including your business needs, the dynamic nature of your environment, and the cost of retraining. By understanding these factors and applying the right strategy, you can ensure your models remain effective and deliver the best results for your business.

If want to learn more about AI and machine learning, check out our online courses at NILG.AI. We offer free previews so you can start learning and implementing AI in your business today. You can also book a meeting with us today so we can help you understand and implement AI in your business.

Like this story?

Subscribe to Our Newsletter

Special offers, latest news and quality content in your inbox once per month.

Signup single post

This field is for validation purposes and should be left unchanged.

Recommended Articles

Link to Leaders Awarded NILG.AI Startup of the Month

NILG.AI is Startup of the Month Link to Leaders awarded NILG.AI the Startup of the Month (check news). Beta-i nominated us after winning two of their Open Innovation challenges: VOXPOP Urban Mobility Initiatives and Re-Source. AI with Geospatial Data At VOXPOP, NILG.AI built an AI-based mobility index for wheelchair users for the municipality of Lisbon […]

Read More
Can Machine Learning Revolutionize Your Business?

Today, the buzz around machine learning (ML) is louder than ever. But what is it exactly, and more importantly, can it revolutionize your business? In essence, ML is a technology that empowers machines to learn from data, improve over time, and make predictive decisions. It has the potential to redefine how businesses operate. In this […]

Read More
Can ‘Old but Gold’ Predictions Minimize AI Costs?

There’s a common pattern in artificial intelligence (AI) where large corporations build massive infrastructures to support their AI use cases. The goal is to make quick predictions and constantly update with new data to scale up your infrastructure. However, this approach often overlooks the trade-off between infrastructure cost and the size of the opportunity that […]

Read More