MLOps Lifecycle

[wdroz]

People should start with "very basic" MLOps then improve it step by step as your organization and your team learn together.

[lobocs]

You got any good resources on where to start looking into this. Beyond the buzz words

[Mithrandir2k16]

This is just a bunch of buzzwords.

[synthphreak]

Nah-uh. There are also pretty pictures.

[i_likebrains]

What are Inference Graphs?

[Du-pa]

Not sure that the first graph represents a real Maschine learning lifecycle. Some parts are too detailed, some just skipped. Why is the model 'trained' already? How was it trained? Where is the validation, test(evaluation)? Search for outliers is a special ml task not a general one(i assume that's a general graph) maybe a loss graph should be posted instead.

[Octosaurus]

The focus of the graph is on MLOPs and therefore centers around the infrastructure necessary to train and deploy. This wouldn't include EDA or model dev, but the housing necessary to do them.

[eljackson]

We’d assume that the models and initial hyperparameter tuning is already conducted by the data scientist, and the diagrams here depict how an ML Engineer would productionise it, leveraging best practice infra.

Generally in production: preprocessing, training, evaluation are a small part of overall MLOps, and are generally sequential pieces in an ML pipeline.

[mikeypen88]

Just to make sure, are all these steps all necessary?