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Persistent Storage and Scalable Compute Power?

I appologize if these questions have been answered...I'm new here and did my best with the search.

I'm interested in training machine learning models and I run into two potential issues:

1) Lack of persistent storage. Is there some and I'm just missing it? I would really like a way to save trained network parameters. Retraining the model every time you spin it up is really not practical (especially in light of #2, below). 

I realize that storage external to QC is not an option because of the risk of users attempting to exfiltrate proprietary data. However, I'm willing to pay for QC internal storage at typical cloud rates (+reasonable markup). I am intersted in equities, so I need to train models on QC to get access to the data. I realize that this creates "lock-in" but given the access to these data sets, it seems like a fair trade.

2) Lack of CPU scaling or GPUs. It's really nice (and often necessary) to accelerate training with more power. Again, I'm willing to pay for the resources should they be made available.

Final comment of my first post: I've poured through all of the API documentation and I must say that's IMHO it's really well designed. It's very easy to understand and use. And extra kudos for opensourcing LEAN! I'm really excited to work within the QC infrastructure, using LEAN. Thank you, QuantConnect.

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