Goto

Collaborating Authors

The Notebook Anti-Pattern - KDnuggets

#artificialintelligence

In the past few years there has been a large increase in tools trying to solve the challenge of bringing machine learning models to production. One thing that these tools seem to have in common is the incorporation of notebooks into production pipelines. This article aims to explain why this drive towards the use of notebooks in production is an anti pattern, giving some suggestions along the way. Let's start by defining what these are, for those readers who haven't been exposed to notebooks, or call them by a different name. Notebooks are web interfaces that allow a user to create documents containing code, visualisations and text.


The notebook anti-pattern

#artificialintelligence

In the past few years there has been a large increase in tools trying to solve the challenge of bringing machine learning models to production. One thing that these tools seem to have in common is the incorporation of notebooks into production pipelines. This article aims to explain why this drive towards the use of notebooks in production is an anti pattern, giving some suggestions along the way. Let's start by defining what these are, for those readers who haven't been exposed to notebooks, or call them by a different name. Notebooks are web interfaces that allow a user to create documents containing code, visualisations and text.


How to use Jupyter Notebooks in 2020 (Part 2: Ecosystem growth)

#artificialintelligence

This is the second of a three-part blog post on the Jupyter Notebook ecosystem. Here, I'll discuss various tools that I use alongside Notebooks, and how I incorporate them in my day-to-day work. You'll find Part One in this link. Let's jump right into it. Recall that in Part One, we identified (1) two directions of ecosystem growth, i.e, cloud adoption and software production, and (2) three forces of change driving the evolution of our tools, especially in the Jupyter Notebook ecosystem: In Part Two, we'll expound upon these key drivers and investigate how the Jupyter Ecosystem grew to respond to these forces--perhaps via a plugin, a new tool, or a new workflow. Lastly, we'll put them together as I share how I use notebooks in my day-to-day.


How Data Scientists Can Tame Jupyter Notebooks for Use in Production Systems

#artificialintelligence

Uncounted pixels have been spilled about how great Jupyter Notebooks are (shameless plug: I've spilled some of those pixels myself). Jupyter Notebooks allow data scientists to quickly iterate as we explore data sets, try different models, visualize trends, and perform many other tasks. We can execute code out-of-order, preserving context as we tweak our programs. We can even convert our notebooks into documents or slides to present to our stakeholders. Jupyter Notebooks help us work through a project from its earliest stages to a point where we can say a great deal.


Beginner's Guide to Jupyter Notebooks for Data Science (with Tips, Tricks!)

#artificialintelligence

One of the most common question people ask is which IDE / environment / tool to use, while working on your data science projects. As you would expect, there is no dearth of options available – from language specific IDEs like R Studio, PyCharm to editors like Sublime Text or Atom – the choice can be intimidating for a beginner. If there is one tool which every data scientist should use or must be comfortable with, it is Jupyter Notebooks (previously known as iPython notebooks as well). Jupyter Notebooks are powerful, versatile, shareable and provide the ability to perform data visualization in the same environment. Jupyter Notebooks allow data scientists to create and share their documents, from codes to full blown reports.