Announcing the Introduction to Amazon Machine Learning Video Course

Would you like an easy introduction to machine learning?  Without downloading any open source software, reading documentation and blog posts, and/or installing and configuring the system?

Amazon Machine Learning is a great way to explore machine learning without having to run any infrastructure.  It lets you build high performance cost effective systems to predict outcomes based on past data.

If you’re interested in learning more about Amazon Machine Learning, you can view my video course on O’Reilly Safari.  Over an hour and a half of video talking about all aspects of Amazon Machine Learning.

This course shows you how to build a model using Amazon Machine Learning (Amazon ML) and use it to make predictions. AWS expert Dan Moore covers the basic types of machine learning, how to prepare your data, and how to make your data available to the Amazon Machine Learning processes. You’ll also learn about evaluating a model for accuracy, using it both for batch and real-time predictions, and using tags to manage environments. Designed for developers and technical marketers new to machine learning and for data scientists interested in using the AWS Amazon ML platform, the course provides hands-on experience building a working predictive model using real data. Learners should obtain an AWS account (free from Amazon) and a basic understanding of AWS concepts before beginning the course.


Heroku 503 errors

One day recently I woke up to a text from our monitoring service (part of minimal heroku operations tasks) saying our application was down.

Darn it.

Here’s the recreation of my steps in troubleshooting this issue:

  • Login, see that the app is indeed down.
  • Look at newrelic.
  • Look at the logs (papertrail!).
  • Look at the deployment history.
  • Note when the issue started–curses, not when I did a deploy.
  • Open a ticket with heroku (after doing some research). (Love their support.)
  • Double check that the database is good and hasn’t hiccuped.
  • Look at the logs more.
  • Add more dynos, see if that helps.
  • Google the error message.
    •  <app-name> heroku/router: at=info method=POST path=”<url>” host=app.thefoodcorridor.com request_id=8a17648f-2d84-46ea-abf2-5903be894a2c fwd=”216.191.191.58″ dyno=web.3 connect=1ms service=4ms status=503 bytes=477 protocol=https“Notice that the issue is being stated right in the log file (passenger request queue filling up).  Here are sample error messages?
    • <app-name> app/web.3: [ 2017-07-12 14:47:44.3688 65/7f652dffd700 age/Cor/Con/CheckoutSession.cpp:261 ]: [Client 3-281] Returning HTTP 503 due to: Request queue full (configured max. size: 100)
  • Find some posts about the error message.  Here and here.
  • Start researching how to increase request queue size.
  • Talk a walk to clear my head.
  • Think about what external services we call, as that seems to be what might cause the request queue to back up.
  • Read another post that says restarting passenger helped.
  • Restart all dynos.
  • Problem disappears.
  • Look at logs more closely.
  • Last dyno to be restarted was the only problematic dyno.
  • Add comment to ticket about this being the cause.
  • Heroku confirms that the issue may have been the dyno: “sometimes individual dynos will hang and cause errors with 503 responses”
  • Write note to customers about the issue explaining how access to app was affected.
  • Lower number of dynos.
  • Breath a sigh of relief.


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