So, what MLflow can do more efficiently, than Tensorflow?
It isn’t a deep learning framework. It’s closer to a LIMS system.
MLflow: An Open Source Machine Learning Platform
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Re: MLflow: An Open Source Machine Learning Platform
#12What 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.
Re: MLflow: An Open Source Machine Learning Platform
#13What are the current open source alternatives to MLflow?
Sacred ( https://github.com/IDSIA/sacred ) FGLab ( https://kaixhin.github.io/FGLab/ ) Metricmachine ( https://github.com/danielwaterworth/metricmachine ) Non-open source: Neptune ( http://neptune.ml ) Aetros ( https://aetros.com/trainer )
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.
Re: MLflow: An Open Source Machine Learning Platform
#14So, what MLflow can do more efficiently, than Tensorflow?
I can't tell if you didn't read the article or are asking for a comparison between this and, say, TF's Experiment class.
Re: MLflow: An Open Source Machine Learning Platform
#15What are the current open source alternatives to MLflow?
Sacred ( https://github.com/IDSIA/sacred ) FGLab ( https://kaixhin.github.io/FGLab/ ) Metricmachine ( https://github.com/danielwaterworth/metricmachine ) Non-open source: Neptune ( http://neptune.ml ) Aetros ( https://aetros.com/trainer )
The open source alternatives you list seem to only provide experimentation logging. ML Flow seems to support more (such as model deployment).
Not to claim that the deployment processes are _good_, just that MLFlow seems more general than these open source alternatives listed here.
Re: MLflow: An Open Source Machine Learning Platform
#16What are the current open source alternatives to MLflow?
Sacred ( https://github.com/IDSIA/sacred ) FGLab ( https://kaixhin.github.io/FGLab/ ) Metricmachine ( https://github.com/danielwaterworth/metricmachine ) Non-open source: Neptune ( http://neptune.ml ) Aetros ( https://aetros.com/trainer )
For deep learning, deepdetect can be useful in dev and prod phase.
Re: MLflow: An Open Source Machine Learning Platform
#17What are the current open source alternatives to MLflow?
https://pachyderm.io is a data science infrastructure framework that has both open source and enterprise versions.
Re: MLflow: An Open Source Machine Learning Platform
#18Earlier quoted context omitted.
Sacred ( https://github.com/IDSIA/sacred ) FGLab ( https://kaixhin.github.io/FGLab/ ) Metricmachine ( https://github.com/danielwaterworth/metricmachine ) Non-open source: Neptune ( http://neptune.ml ) Aetros ( https://aetros.com/trainer )
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.