Amazon SageMaker is a Machine-Learning-as a-Service (MLaaS) framework that focuses on ML model development and automation, for example model serving. The daily mood I realize that I am looking much at Cloud Native technology but not using much of Cloud provider solutions, which is probably as much important if you do not want to re-invent the wheel at application level. As already discussed in my previous post about "ML model deployment", we've been using both AWS Databricks and Amazon SageMaker as part of our internal Data lake project. In my last post , I looked at MLflow which is actually the relevant part of Databricks for deployment. Today I am looking at Amazon SageMaker which is indeed integrated by MLFlow for model deployment via the SageMaker SDK, but also offers a slightly different tooling and approach for development and operations. Why AWS for ML AWS currently offers one of the largest set of managed capabilities for Machine Learning . The offer consists ...
Learnings & thoughts collected on-the-job