IPython provides a rich toolkit to help you make the most of using Python interactively. It provides a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends. 
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IPython’s interactive shell (ipython), has the following goals, amongst others:
<ol>
<li>Provide an interactive shell superior to Python’s default. IPython
has many features for tab-completion, object introspection, system shell
access, command history retrieval across sessions, and its own special
command system for adding functionality when working interactively. It
tries to be a very efficient environment both for Python code development
and for exploration of problems using Python objects (in situations like
data analysis).</li>
<li>Serve as an embeddable, ready to use interpreter for your own
programs. An interactive IPython shell can be started with a single call
from inside another program, providing access to the current namespace.
This can be very useful both for debugging purposes and for situations
where a blend of batch-processing and interactive exploration are needed.</li>
<li>Offer a flexible framework which can be used as the base
environment for working with other systems, with Python as the underlying
bridge language. Specifically scientific environments like Mathematica,
IDL and Matlab inspired its design, but similar ideas can be
useful in many fields.</li>
<li>Allow interactive testing of threaded graphical toolkits. IPython
has support for interactive, non-blocking control of GTK, Qt, WX, GLUT, and
OS X applications via special threading flags. The normal Python
shell can only do this for Tkinter applications.</li>
</ol>

IPython provides a rich toolkit to help you make the most of using Python interactively. It provides a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends. 

IPython - Interactive Computing

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow is a platform for data scientists who want to build and experiment with ML pipelines. Kubeflow is also for ML engineers and operational teams who want to deploy ML systems to various environments for development, testing, and production-level serving.

kubeflow - Machine Learning Toolkit for Kubernetes

The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. It supports over 40 programming languages. 
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Code can produce rich output such as images, videos, LaTeX, and JavaScript. Interactive widgets can be used to manipulate and visualize data in realtime.

The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. It supports over 40 programming languages. 

Jupyter - Web-based notebook environment for interactive computing

A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more. 

Zeppelin - Multi-purpose Notebook

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IPython - Interactive Computing

kubeflow - Machine Learning Toolkit for Kubernetes

Jupyter - Web-based notebook environment for interactive computing

Zeppelin - Multi-purpose Notebook

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IPython - Interactive Computing

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Jupyter - Web-based notebook environment for interactive computing

Zeppelin - Multi-purpose Notebook

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