I work as a Senior Software Engineer and DevOps for a AI consultancy, and I've been dealing with MLOps for the past 2 years. I'm mostly aligned with what the a

I work as a Senior Software Engineer and DevOps for a AI consultancy, and I've been dealing with MLOps for the past 2 years. I'm mostly aligned with what the article says, MLOps today is definitively in a frazzled state. However, I disagree on the following points : - Google and al. are not good example to follow for ML deployment best practices. Sure their sophistication is higher, but they also have a lot more staff to handle "the other side" of modelling : Data, Infrastructure, Tooling. They build tools to suit their needs, as big SaaS products holding *bytes of already organized data ready to be used for data science, and it's a really different perspective than a large retailer trying to get better sales forecasts. - Vendors from all sides have really, really bad fitting products for Data Science and ML in general. "Platforms" are trying to profit off MLOps with a commercial product claiming to be the silver bullet to every pain your team has. Three months later, it's just another life-sucking lock-in with a list of tickets to be addressed. We really miss a new "Docker" here. Have a few examples: Databricks ? It's a Spark-as-a-Service platform with the worst APIs you could imagine. Git-for-data vendors ? They don't understand Git nor Data : is a model data or code ? Both ? Finally, ML at Reasonable Scale builds on top of regular software engineering best-practices. If you don't usually store a shell script's output in a repository, then you should do the same for a notebook. Same goes for Idempotency, Reproducibility (of model training), Composability (of pipeline steps), (data) Versioning, etc..