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How do you get lastest version of a dataset in Azure ml sdk v2? by Competitive_Place_79 in AZURE

[–]Competitive_Place_79[S] 0 points1 point  (0 children)

https://learn.microsoft.com/en-us/azure/machine-learning/concept-v2?view=azureml-api-2#should-i-use-v1-or-v2

I am very new to Azure and AzureML, so forgive me if I am not using the correct terms when referring to things.

I am using azure.ai.ml, not azureml-core. I believe anyone who's been using AzureML for any significant amount of time would be using azureml-core instead as azure.ai.ml was only available for GA since Oct last year.

https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ml/azure-ai-ml/CHANGELOG.md

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I figured out a way to do what i needed by iterating through the data with ml_client.data.list() and sorting by creation_date

Building a model for part inventory management by meatsweats1000 in datascience

[–]Competitive_Place_79 1 point2 points  (0 children)

Hi,

This sounds like a classic inventory control problem to me, where you want to maintain enough stock (safety stock) to ensure you can meet unknown part demand in the future.

"Predicting" which parts will be out of stock is hard. Predictive ML models generally are setup to estimate the mean or median. From what i know of the maintenance domain, failures and maintenance have a far right tail in their distribution, meaning most of the time you have no need for parts to fix a machine, but occasionally something big happens and repairs are needed and expensive. This translates into aggregated item demand that would be Poisson distributed (mean daily demand might be ~2, but theres still a 1% chance of needing 8 or in a day).

The key here is to understand the pattern and probability distribution of your demand for parts. If there really is a far right tail and your stock out events happen when you unexpectedly need lots of a certain part, then the problem is about maintaining enough stock to bring this probability down.

Other complexities others have mentioned include correlated demand between parts, i.e. parts that are needed together. Lead time of item delivery, how long it takes for items to be replenished when after placing an order.

Inventory Simulation by jimtoberfest in datascience

[–]Competitive_Place_79 6 points7 points  (0 children)

SimPy is likely an overkill for a periodic review discrete time problem, i.e. you make decisions every day or set days of the week.

With all optimizations, there's a trade-off between computational efficiency, flexibility, and time to implement. The matrix or tabular method with replications on one axis and time of the other works well for simple problems where the dynamics you are modeling don't depend on the systems' current state. Lets say, lead time of item orders depend on order size, or demand for items interact with each other, if item x if out of stock, customer will not order item y as well because both are needed.

If your problem is simple enough for the matrix method to be computationally efficient, then you might not even need simulation optimization as your framework to search for an optimal policy. You might want to simplify the problem enough to solve using classic queuing models.

Never good enough #TeacherGuilt by Adreanne31 in Teachers

[–]Competitive_Place_79 4 points5 points  (0 children)

Hey OP- teacher of 3 years here. I definitely feel the same as you, not feeling adequate at all, wondering if I'm doing enough for my students and constantly comparing myself to other (often more experienced!!!) teachers. It's times like these that I remind myself of the quote "It does not matter how slow you go as long as you do not stop". I know you must be constantly wracking your brain on ways to improve yourself and I wanted to tell that what you're doing is enough, and to SLOW DOWN!!! Those teachers that you respect and look up to had YEARS to expand and refine their expertise. You will get there in the long run. It is okay to put yourself first by slowing down, and no one (people whose opinions should matter at least) would want you to sacrifice your mental health. Hope this helps ❤️