Coinbase said on Monday that it has developed and deployed a machine learning model that predicts spikes in user traffic and automatically scales its resources, preventing downtime and increasing platform efficiency.

The AI solution aims to address the problem of platform crashes during unpredictable traffic surges, which have plagued the platform during volatile market conditions.

The Need for Predictive Scaling

"Starting to scale when traffic is already high is often too late," the blog explains. "We developed an automatic scaling solution that uses machine learning to predict the traffic spikes and trigger a scale-up before the traffic arrives." The exchange says the model has already proven its worth during volatile market periods.

"As traffic increased, so did our scale target, doubling twice a few hours before peak traffic," Coinbase wrote. "The model continued scaling up and down with the daily usage pattern until volatility decreased and there was no longer a need to scale up."

How the Model Works

The model's task is to provide a signal with 60 60-minute lead time before a traffic spike. Coinbase previously tried a time-series forecasting model that attempted to predict traffic levels 60 minutes into the future. However, this approach could have been more effective due to the lag time in the underlying statistics.

Instead, Coinbase transformed the problem into a longer-term classification one. The new model leverages external signals, such as price fluctuations in major cryptocurrencies like Bitcoin and Ethereum.

The model tries to answer the question of whether traffic will exceed a certain threshold level in the next few hours. This approach has significantly improved accuracy.

"The key insight: if cryptocurrency price volatility is high and the current traffic is approaching the target level at a faster rate than anticipated, then the likelihood of a traffic spike is increased," the blog states.

Balancing Accuracy and Efficiency

Coinbase explains that its AI model was designed to balance avoiding missed spikes and reducing false alerts. This is an important step because triggering false alerts may result in wasted resources, whereas too much reduction of false alerts may end up with an inaccurate model that does not serve its purpose—and the exchange crashing during high traffic periods as a direct consequence.

Historical Challenges

Coinbase's history with market volatility could be a lot better. The exchange is known for outages and technical issues during critical moments in crypto history.

For example, on May 14, 2024, Coinbase experienced a significant outage lasting more than three hours, affecting desktop and mobile platforms. Users encountered a '503 service temporarily unavailable' message on the website and incorrect 'planned maintenance' notifications on the mobile app.

Just two months before that, when Bitcoin was on its way to reaching its ATH, the exchange crashed, leaving thousands unable to realize their gains.

"We are encouraged by the market's excitement and are continuing to double down on the capacity and resilience of our systems," a Coinbase spokesperson told Decrypt then.

Looking Forward

Coinbase has continuously said it has taken steps to resolve past issues. Technical upgrades and infrastructure improvements have been a primary focus.

The company has invested in enhancing its server capacity and optimizing its software architecture to handle high traffic volumes better. But considering the problems are caused by traffic spikes and not by steady traffic growth, the use of AI to predict unusual traffic and automatically scale databases seems to be the most optimal approach—if proven effective. We need to wait until the next price swing to see whether this AI algorithm can beat a flood of crypto traders.