ml-engineer-pro video for you are employed by a gaming company with millions of customers worldwide. Your games offer a real-time chat feature that enables
You are employed by a gaming company with millions of customers worldwide. Your games offer a real-time chat feature that enables players to communicate with each other in over 20 languages. These messages are translated in real time using the Cloud Translation API. Your task is to create an ML system that moderates the chat in real time while ensuring consistent performance across various languages, all without altering the serving infrastructure. You initially trained a model using an in-house word2vec model to embed the chat messages translated by the Cloud Translation API. However, this model exhibits notable variations in performance among different languages. How can you enhance the model's performance in this scenario?