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AutoViML creates innovative Open Source libraries to make data scientists' and machine learning engineers' lives easier and more productive!

Our innovative libraries so far:

• 🤝 AutoViz Automatically Visualizes any dataset, any size with a single line of code. Now with Bokeh and Holoviews it can make your charts and dashboards interactive!
• 🤝 Auto_ViML Automatically builds multiple ML models with a single line of code. Uses scikit-learn, XGBoost and CatBoost.
• 🤝 Auto_TS Automatically builds ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with DASK to handle millions of rows.
• 🤝 Featurewiz Uses advanced feature engineering strategies and select the best features from your data set fast with a single line of code. Now updated with DASK to handle millions of rows.
• 🤝 Deep_AutoViML Builds tensorflow keras models and pipelines for any data set, any size with text, image and tabular data, with a single line of code.
• 🤝 lazytransform Automatically transform all categorical, date-time, NLP variables to numeric in a single line of code, for any data, set any size.

April-2022: Released a major new python library "lazytransform" #featureengineering #featureselection

On April 3, 2022, we released a major new Python library called "lazytransform" that will automatically transform all categorical, date-time, NLP variables to numeric in a single line of code, for any data, set any size.

Jan-2022: Major upgrade to featurewiz: you can now perform feature selection thru fit and transform #MLOps #featureselection

As of version 0.0.90, featurewiz has a scikit-learn compatible feature selection transformer called FeatureWiz. You can use it to perform fit and predict as follows. You will get a Scikit-Learn Transformer object that you can add it to other data pipelines in MLops to select the top variables from your dataset.

Dec-23-2021 Update: AutoViz now does Wordclouds! #autoviz #wordcloud

AutoViz can now create Wordclouds automatically for your NLP variables in data. It detects NLP variables automatically and creates wordclouds for them.

Dec 21, 2021: AutoViz now runs on Docker containers as part of MLOps pipelines. Check out Orchest.io

We are excited to announce that AutoViz and Deep_AutoViML are now available as containerized applications on Docker. This means that you can build data pipelines using a fantastic tool like orchest.io to build MLOps pipelines visually. Here are two sample pipelines we have created:

AutoViz pipeline: https://lnkd.in/g5uC-z66 Deep_AutoViML pipeline: https://lnkd.in/gdnWTqCG

You can find more examples and a wonderful video on orchest's web site

Dec-17-2021 AutoViz now uses HoloViews to display dashboards with Bokeh and save them as Dynamic HTML for web serving #HTML #Bokeh #Holoviews

Now you can use AutoViz to create Interactive Bokeh charts and dashboards (see below) either in Jupyter Notebooks or in the browser. Use chart_format as follows:

• chart_format='bokeh': interactive Bokeh dashboards are plotted in Jupyter Notebooks.
• chart_format='server', dashboards will pop up for each kind of chart on your web browser.
• chart_format='html', interactive Bokeh charts will be silently saved as Dynamic HTML files under AutoViz_Plots directory

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