MLflow: An Open Source Machine Learning Platform

https://databricks.com/blog/2018/06/05/introducing-mlflow-an-open-source-machine-learning-platform.html

by r4um • 8 years ago

135 28 8 years ago

databricks.com

tafycent• 8 years ago

FYI typo: mlflow.log_atrifact("roc.png")

tchalla• 8 years ago

What are the current open source alternatives to MLflow?

agnokapathetic• 8 years ago

https://www.mlflow.org is open source.

denzil_correa• 8 years ago

I understand. However, there could be more than one open source ML platforms. Hence, open source alternatives to MLFlow doesn't imply MLFlow isn't open source.

mmq2• 8 years ago

Polyaxon, https://github.com/polyaxon/polyaxon, is an alternative project to MLFlow that is also open source.

disclaimer: I am the author of Polyaxon.

syllogism• 8 years ago

I've been using Polyaxon to train models for spaCy. It uses Kubernetes, so it's easy to set it up to work very efficiently on Google Compute Engine. For instance, you can set it to use pre-emptible instances, which makes the experiments very cheap.

brylie3• 8 years ago

Any plans to support scikit?

hcrisp• 8 years ago

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)

__bee• 8 years ago

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.

sbpayne• 8 years ago

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.

pilooch• 8 years ago

For deep learning, deepdetect can be useful in dev and prod phase.

TheIronYuppie• 8 years ago

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.

ddutta• 8 years ago

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.

daveguy• 8 years ago

https://pachyderm.io is a data science infrastructure framework that has both open source and enterprise versions.

allisterb• 8 years ago

ClassifyBot https://github.com/allisterb/ClassifyBot

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

shakedown1• 8 years ago

A couple more alternatives:

H2o: https://www.h2o.ai/h2o/ Data Robot: https://www.datarobot.com/

marcuniq• 8 years ago

RiseML seems to be another alternative: https://riseml.com/

Not open source though

phltech• 8 years ago

Also not open source, but looks similar to what ParallelM does http://www.parallelm.com

riku_iki• 8 years ago

So, what MLflow can do more efficiently, than Tensorflow?

stochastic_monk• 8 years ago

It isn’t a deep learning framework. It’s closer to a LIMS system.

nicolewhite• 8 years ago

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.

axilmar• 8 years ago

There is also Orange, although I am not sure it is 100% related to MLFlow. Orange is a joy to use though, so even if it doesn't solve all the problems solved by MLflow, it's worth to be mentioned in this context.

rememberlenny• 8 years ago

If you are looking for a dependable, scalable, closed-source option, check out http://www.comet.ml (the thing I work on).

The focus with Comet.ml is more on experiment tracking and hyperparameter optimization rather than model deployment. We make it very easy to compare your experiments results, code, and hashed datasets for better reproducibility.

We have a one-line integration with your existing machine learning code and make it stupid simple to start tracking your experiments.

All you do is:

  > import comet_ml
  > experiment = Experiment(api_key="MY_API_KEY")

_boom_

Comet.ml supports many libraries (keras, tensorflow, scikit-learn, custom-built code spaghetti, and everything else that makes you a ML wizard/unicorn/armored flaming hippopotamus).

  ++ Its free for public projects and academics.

MLflow: An Open Source Machine Learning Platform

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