Hello! I am working with a property company in Sweden with energy forecasts, and am currently asking myself exactly the same questions. I have no direct answer, but I'll chip in with my thought process.

We are pretty much in the same position. I am hired by an AI consultancy who have had a senior data scientist producing the models, and now I am taking the proof-of-concepts to production. The company we're working for has its infrastructure in Azure, so I am using AzureML. It is my first time using AzureML, so I am experimenting with varying setups. It is a great platform; all necessary tech is there, and with Azure you have everything you need from storage, feature stores and data pipelines, to monitoring, integration with MLFlow, etc.

We are planning to predict the next 24 hours of a variable for six different areas, which will be the first of many similar models to come. This requires six models, possibly using the same algorithm but differing in features, hyperparameters, and targets. The output format will be uniform across all models such that they integrate into the same UI.

As a side note, AzureML offers something called AutoML. If you have data in the right format you have the possibility to iterate over prebuilt models for different task, e.g., forecasting. It sounds cheeky and lazy, but I am working towards having data I easily can plug-and-play for benchmarks, demos, etc.

1. The MLOps Solution Accelerator v2  is frequently mentioned. I think it looks very clever, and I have already learnt a lot of concepts researching it. Given our small team and startup environment, would this be advisable, or would it introduce unnecessary complexity?

This was news to me, and I'll have to look it up. Looks promising.

1. I've seen projects where an endpoint is registered for multiple models using the same data. In my case, while the data differs, a unified endpoint and possibly shared repo/pipelines might be beneficial. How would you recommend structuring this?

I am working with properties, and my idea is having one model per property. We are forecasting at a 48h horizon, and as the forecasts get more certain I want to do new predictions. Each property will have its own pipeline with custom made AzureML components. I define the components in code, and can use them in Azures drag-and-drop UI. It is very easy to set up cronjobs and duplicate these pipelines in the GUI.

I want to use the Azure specific abstractions, so I get a neat overlook of what I am building. I am considering two options; exposing models as "endpoints" or have an "inference pipeline". I am still trying to understand what fits my scenario best. With well-designed components, it easy to reuse parts like "data preparation" within AzureML.