Taking on the ML pipeline challenge: why data scientists need to own their ML workflows in production

1

neptune.ai

So, if you even want to use MLFlow to track your experiments, run the pipeline on Airflow, and then deploy a model to a Neptune Model Registry, ZenML will facilitate this MLOps Stack for you. This decision can be made jointly by the data scientists and engineers. As ZenML is a framework, custom pieces of the puzzle can also be added here to accommodate legacy infrastructure.

#Data Science And Machine Learning #Data Science Notebooks #Machine Learning Tools 22 social mentions

2

ZenML

ZenML is an open-source MLOps Pipeline Framework built specifically to address the problems above. Let’s break it down what a MLOps Pipeline Framework means:.

#Developer Tools #AI #GitHub 10 social mentions

3

Apache Airflow

So, if you even want to use MLFlow to track your experiments, run the pipeline on Airflow, and then deploy a model to a Neptune Model Registry, ZenML will facilitate this MLOps Stack for you. This decision can be made jointly by the data scientists and engineers. As ZenML is a framework, custom pieces of the puzzle can also be added here to accommodate legacy infrastructure.

#Workflows #Workflow Automation #Data Pipelines 66 social mentions