21–30 of 31 posts
What are the current open source alternatives to MLflow?
I would imagine Kubeflow ( https://github.com/kubeflow/kubeflow ) would be complimentary (e.g. run MLFlow on top of it) - they claim platform neutrality. Disclosure: I work at Google on Kubeflow.
What are the current open source alternatives to MLflow?
Designed to automate and make repeatable different stages in classification pipelines. Written in .NET but agnostic about language or framework. Embeds a Python interpreter and can interface with Java or R.
Disclaimer: I am a walrus
What are the current open source alternatives to MLflow?
H2o: https://www.h2o.ai/h2o/ Data Robot: https://www.datarobot.com/
What are the current open source alternatives to MLflow?
Not open source though
Earlier quoted context omitted.
How about SageMaker, Can we include it in this list. I played with SageMaker sometime ago and it helps you build a whole pipeline to host your models, in addition to host your notebook and bridge the gap between data scientists and data engineers.
Anecdotally, we considered using the hosted versions of Jupyter and Apache Zeppelin that are part of AWS SageMaker and EMR. We couldn't figure out a simple/familiar workflow for keeping the notebooks under version control. So, we agreed to run the notebooks locally, use a familiar Git-based workflow, and interact with the AWS infrastructure through the local notebook instances.
Earlier quoted context omitted.
I would imagine Kubeflow ( https://github.com/kubeflow/kubeflow ) would be complimentary (e.g. run MLFlow on top of it) - they claim platform neutrality. Disclosure: I work at Google on Kubeflow.
Yes, Kubeflow is a vey promising platform for ml lifecycle management on kubernetes. The combination of kubernetes, istio and kubeflow could enable other higher layer workflow tools (mlflow, h2o etc). This space is early.
What are the current open source alternatives to MLflow?
Earlier quoted context omitted.
Anecdotally, we considered using the hosted versions of Jupyter and Apache Zeppelin that are part of AWS SageMaker and EMR. We couldn't figure out a simple/familiar workflow for keeping the notebooks under version control. So, we agreed to run the notebooks locally, use a familiar Git-based workflow, and interact with the AWS infrastructure through the local notebook instances.
Does Zeppelin work naturally with git? I've been struggling to get the right setup with just Jupyter