MLflow: An Open Source Machine Learning Platform

If you are looking for a dependable, scalable, closed-source option, check out <a href="http:&#x2F;&#x2F;www.comet.ml" rel="nofollow">http:&#x2F;&#x2F;www.comet.ml</a> (the thing I work on).<p>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.<p>We have a one-line integration with your existing machine learning code and make it stupid simple to start tracking your experiments.<p>All you do is:<p><pre><code> &gt; import comet_ml &gt; experiment = Experiment(api_key=&quot;MY_API_KEY&quot;) </code></pre> _<i></i>boom<i></i>_<p>Comet.ml supports many libraries (keras, tensorflow, scikit-learn, custom-built code spaghetti, and everything else that makes you a ML wizard&#x2F;unicorn&#x2F;armored flaming hippopotamus).<p><pre><code> ++ Its free for public projects and academics.</code></pre>

What are the current open source alternatives to MLflow?

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&#x27;t solve all the problems solved by MLflow, it&#x27;s worth to be mentioned in this context.

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

FYI typo: mlflow.log_atrifact(&quot;roc.png&quot;)