ml-engineer-pro video for you've recently created a custom neural network that relies on essential dependencies unique to your organization's framework. Now,
You've recently created a custom neural network that relies on essential dependencies unique to your organization's framework. Now, you want to train this model using a managed training service in Google Cloud. However, there's a challenge: the ML framework and its related dependencies aren't compatible with AI Platform Training. Additionally, both your model and data exceed the capacity of a single machine's memory. Your preferred ML framework is designed around a distribution structure involving schedulers, workers, and servers. What steps should you take in this situation?