The newbie cloud architect diary

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 ...

Because a Machine Learning model is a living asset, it is best-practice to automate its development  lifecycle to ensure repeatability. The daily mood Today was my weekly office day and I had the opportunity to meet a colleague from the SRE team, that I had actually not seen for a long time anymore. He told me about the SRE organisation, pains and achievements, which I found very insightfull. We also joined 3 other colleagues for quite a long lunch break at the terrasse of a restaurant. None of us had much social contact during the last months of lock-down, and it looks like we all enjoyed meeting again. Beside that, I am now looking at some ML tooling for enforcing best-practices and operationalization, that we are using in a project (see my previous post on ML model deployment) and which I was not yet much familiar with. First candidate MLflow is actually pretty straight forward, at least in theory. What is MLflow MLflow is a ML lifecycle management platform written in Python. ...