Is probably the worst ML service of the major cloud providers: Documentation is crappy, confusing and some times outdated (real mess with passing data objects between pipeline steps) Data drift and model monitoring capabilities extremely limited and in preview since 3 years ago. Total mess with AzureML SDK 2.0. Im sorry microsoft but I will not have my ml pipelines as yaml files. This is delusional. Also neither ChatGPT can answer questions about this version of the SDK because literally no one is using it.... 2 years behind for a centralized model registry (now in preview) Lack of integration between model registry events and azure devops (you need to create a custom azure function to listen to this events and then trigger your CI pipelines). There is an extension for this but it does not work with multiple models. This is important because if your models take more than 6h to train the CI pipelines will fail in DevOps if you use MS Hosted Agents. So deacoplating the CD part from the model generation is usually a good idea. Running spark is a lotery (now in preview again). Other alternatives like SageMaker have allowed you to do that since 2019. There is an unsupported library called RayOnAML to run ray in AML. Is bullshit, it doesn't work in an stable manner. Constant failure of VSCode connection to azureML instances, I recommend you to use JupyterLab (despite of being not optimal) as if you use notebooks with VSCode they will disconnect time to time and you will have to reload everything again. Lack of spot GPU instances (the fail 70% of the time). AML pipelines do not have conditionals, you will always need a high level orchestrator (ADF/LogicApps) to orchestrate complex workflows. I could probably write a blog post with thousands of bad product decisions, unresolved bugs and lack of functionalities. Under any circumstances I will choose this service. PS: I am a senior data scientist with a couple of cloud certifications and strong software designs pattern knowledge and MLOps. I have put more than 7 systems in production with AML. More on reddit.com

What do you mean exactly with "build an AI model from scratch"? You don't have to use anything drag & drop. Everything works via their api/sdk. It's basically a collection of tools that are commonly used (or are similar to) in ML (ops). Preprocessing/training/evaluation you could do using their pipelines, and you can even provide your own docker images / environments / etc. More on reddit.com

All things authentications. Managed Identities. RBACs in Azure, Service Principals, App Identities. If you truly understand this doing everything on Azure is a breeze. More on reddit.com

Seems really interesting for people who are trying to build a product and don't want to spend money or time to get dedicated ML developers. I don't know if they also have a C# library as well, but I could see that being useful for production deployment. Also might end up being a nice ecosystem for ML developers - I think they will support "selling" your API to other users, like if you had a really good trained model. Very much in line with what a bunch of startups are trying to do right now - ML as a black box API.

I really don't see it replacing R, Python, or Julia for algorithm development though.

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