Welcome to jeyn, jeyn is an open source framework that aims at providing an infrastructure for production ML applications and give everything a DS team needs to be able to hit the ground running with as little dev-ops knowledge as possible.

As of writting jeyn is still in its infency and very much not ready to be used in any kind of production context.

Jeyn's python sdk is based around several main abastractions that you will interact with to build your machine learning pipelines

• dataset formula a dataset formula is like a cooking reciepe for a given dataset, it tells other jeyn users how your dataset has been built for instance if it is a streaming pipeline that updates files regularly. Or another dataset passed through a model.
• dataset batch: If we think of the dataset formula as a cooking reciepe a dataset batch is batch of cookies made with a specific formula. A model will always be trained on a precise batch and record this batch for recproducability purposes.
• data catalog: it's the schema of any inputs, our outputs of a jeyn dataset or model.

• model checkpoint: a model checkpoint is a saved model. A set of weights that have been saved after a training
• model serializers: a way of going from a python object to a checkpoint's bytes as well as the other way around.
• machine learning use case: a machine learning use case is basically a way to tell that one or several model checkpoints are more or less "the same". Not in that they'll produce the same output (that's a checkpoint) but that they should be used the same.

All the machine learning abstractions stated above are built withing jeyn to be converted to two lower level abstractions: artefacts and relationships.

• an artefact is basically a thing that is saved.
• a relationship links two artefacts together.

when building your pipeline, your operations always need to be convert their outputs to an artefact. To help you with this jeyn provides various stores (model_store, dataset_store that you can use) to save your models, checkpoints, batches, ect and they will return an artefact you can then output so that jeyn will know how to reload them in subsequent operations.

jeyn is based on 2 major open source technologies:

• dapr: a framework that abstracts cloud implementation to be able to have the same code or architecture running in different setps with a change of configuration
• Argo: to run your machine learning workflows.