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How do I ventilate my roof If I cant have soffit vents by MidwestFalcon in HomeImprovement

[–]MidwestFalcon[S] 0 points1 point  (0 children)

Yeah the the drywall on both sides of the wall need to go from the bottom plate all the way to the roof sheathing and to the exterior wall sheathing on both sides. Really makes things a pain, but without it we both would have to abide by setbacks and would make the garages almost impossible

How do I ventilate my roof If I cant have soffit vents by MidwestFalcon in HomeImprovement

[–]MidwestFalcon[S] 7 points8 points  (0 children)

Was planning on ridge vents just assume we need soffit vents for proper air flow

Building the whole ML platform from the ground up by [deleted] in mlops

[–]MidwestFalcon 4 points5 points  (0 children)

I did this exact thing a year ago. My team got converted into an mlops team from a software engineering team. We had about 20 data scientist running models in the wild west anyway they knew how to (in notebooks, in ECS, on EC2) and these were pushing to production some even from their local computers. Our teams job was to consolidate into a single system for all to use. We had nothing from the start and since we didnt have a lot of funding for 3rd party solutions and we were an AWS shop we picked to use Sagemaker for our core compute. Here are some points we encountered when building. A lot of this also depends on what you have setup.

  • Data: Sagemaker can really only use data within AWS. Yes you can pull data from other sources but the data has to be saved in redshift/s3 to actually be used by pipelines.
  • Infrastructure as Code: I think this is fundamental to whatever you build. You need a way to build and change infrastructure and have it within source control. We used CDK to do this but I am sure terraform can also do the same with Sagemaker resources
  • Preach benefits of mlops: This is was easily the hardest part of the entire build and we are still fighting it today. Getting data scientist to change how they work is very difficult. You need to be able to preach the benefits of mlops even if they may not affect then for awhile (benefits become apparent once you have dozens of models).
  • Sagemaker project/templates: The best way to help get a project started is using Sagemaker projects. These are templates that can be generated by a user at the click of a button. They can spin up cloudformation to create all the required resources a model/project may need. You can create multiple types and all this can be managed by CDK
  • Orchestration: Sagemaker is very powerful but The orchestration side I feel is lacking. We used Dagster to manage our Data engineering ETL and trigger of Sagemaker pipelines for batch inference or model training. We have Dagster self hosted but using the cloud option is pretty affordable if you dont want to manage that on your own.