As a data scientist, you often create visualizations that tell a story about your data. But you may feel limited by the fact that this visualizations live exclusively in your jupyter notebook or python file. Perhaps you’ve tried exporting them as
jpg files using
matplotlib, but that just feels insufficient. There’s a better way: create a static website using Github Pages, and populate it with
html files of your data visualizations using
Sign up for a free Heroku account
After you install the CLI, run the
heroku login command. You’ll be prompted to enter any key to go to your web browser to complete login. The CLI will then log you in automatically.
Fork & clone this repository on github. Then navigate into the folder you just cloned with
cd flying-dog-beers and explore the files with
ls -l. …
Note: You can view this tutorial as a video on youtube.
How does a machine learning engineer make their model results accessible to end-users via the web? This once-daunting task is now pretty straightforward using a combination of AWS services, especially SageMaker and Lambda.
Amazon SageMaker is a powerful tool for machine learning: it provides an impressive stable of built-in algorithms, a user interface powered by jupyter notebooks, and the flexibility of rapidly training and deploying ML models on a massive range of AWS EC2 compute instances. But even the most accurate model provides no benefit if it’s inaccessible to…
This walk-through is designed to help newcomers become familiar with using an AWS Lambda function in combination with an S3 bucket and IAM roles. The walk-through assumes you already have an AWS account and some familiarity with AWS services and the console. All of the information in this post was recycled from the work of previous authors, and full credit goes to them.
I’m particularly indebted to the blog post by Nidhin kumar, and I reuse all of his work in this post. …
In a previous blog post I talked about how you can use AWS S3 to host a static website. While that approach is efficient and cost-effective, it doesn’t allow you to protect your website with a username and password. In the post, we’ll talk about how to add CloudFront and a Lambda function to your S3 website in order to provide greater security.
Amazon S3 is an inexpensive and powerful way to host a static website — no web server required! S3 provides high availability so you don’t have to worry about automatic scaling or unexpected spikes in user demand. It also provides virtually unlimited storage.
Start off by cloning this github repository to a folder on your laptop. It’s a bare-bones webpage example. Then update
index.html and other files as you like. https://github.com/austinlasseter/simple-website
Once you’re happy with the way your simple website looks, upload all the contents of your folder to an S3 bucket. …
This is a step-by-step guide to deploying your first Python app. It’s intended for a complete beginner.
Start out on github — a development platform for sharing and developing code. After signing up for a free github account, fork my repo — it will have all the files you need to get started. It’s easy: at the top right, click on the
fork button (note that in the screenshot it’s greyed out for me because I can’t fork my own repo):
After a brief wait, you should now see the same repo but under your own name, not mine. While…
In a new folder on your laptop, create a virtual environment:
Activate the virtual environment (on a Mac):
Activate the virtual environment (this is for Windows):
Install plotly dash in your virtual environment:
pip install dash
Create a text file called
requirements.txt with a list of programs required by your virtual environment, as follows:
pip freeze > requirements.txt
Create a python program called
application.py with the following Dash code. Note that two items in here —
application = app.server and
application.run(port=8080) are specific to AWS Elastic Beanstalk. …
I recently started building dashboard for my python programs using the Dash framework by Plotly. Here are the steps I took to deploy a simple dashboard on AWS Elastic Beanstalk. Full credit to previous authors whose excellent work led me to this point:
What’s even more fun than riding a BikeShare bike down the Mall on a sunny afternoon? Analyzing the data about BikeShare bikes! It’s available to the public and easy to get, and BikeShare actively encourages the curious to look kick the tires and under the hood (er, basket) of their data: ergo, the existence of Bikeshare Hack Night, which combines the panache of data science with the dernier cri of The Black Cat.
We looked at data from the 4th quarter of 2017. Here’s what we found:
How long were most bike trips?