Jupyter is Ready for Production; As Is. Turn your draft Notebooks to production-ready Kubeflow Pipelines without writing a single line of code
A Machine Learning project usually consists of multiple, interconnected steps: data acquisition, data processing, data modelling, fine-tuning, testing etc. Each of these steps can be a separate process, running at its own cadence, with clearly defined inputs and outputs. Thus, data scientists and ML engineers tend to think these projects like pipelines.
The ideal scenario for a data scientist would be transforming the experimentation environment that a Jupyter Notebook provides, into production-ready ML pipelines.
However, an ML pipeline is a tricky beast to code; connections to data sources, passing the right inputs to each step, serialize the outputs, checkpointing, figuring out the dependencies… Wouldn’t it be great if we could automate the drudgery of boilerplate code needed to configure the workflow execution?
The ideal scenario for a data scientist would be transforming the experimentation environment that a Jupyter Notebook provides, into production-ready ML pipelines. Well, we can do that today. Moreover, we can do that without writing a single line of code.
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