Forecasting with LSTM + MLJ How to ? (A detailed guide or tutorial is needed)

For a future task/project (starting this late spring) we will be required to create and deploy a network for predictive analysis on a large time-series dataset with multiple complex features.
The tools of work (set by my hierarchy/CTO) will be Julia env with MLJ/Flux libraries and LSTM model.

I’ve been assigned (as Product Architect and main developer) to lead this task and now I am at the prospective stage (understanding the problem, learning, studying, business needs, gathering information, designing the product, etc.).

My level of experience/skills with Julia is quite good but I’ve got absolutely no experience on ML/AI with Julia. I have general knowledge of ML, I had several past encounters with TensorFlow and specially with Google’s AutoML. My team consists of two senior developers (C++, Julia, Python) with no AI/ML experience, a data analyst and a theoretical AI/ML “expert” (a university-level scientist with no practical - application development skills).

Therefore I started to study/practice ML on Julia (plenty of documentation online). But I have the feeling that I’m going around in circles and the time is running out, I will be asked soon to deliver the draft with the architecture of the product including the business needs and resource requirements.

What I actually need is a precise step-by-step tutorial, preferably a full example on how to deploy a LSTM network on Julia from A to Z on a dummy dataset.

Thanks for you help.

You can have a look on model zoo for a step -by step examples with various recurrent neural networks.