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My Ignite Presentation

Now for something completely different…

I participated in Ignite Boulder 40 in December 2019. I gave a talk about perennial vegetables.

It was a blast because it was largely out of my comfort zone. Yes, I’d spoken in public a couple of times, but in front of the entire Boulder theater? Yes, I’d given talks, but remembering everything and having no speaker notes? Yes, I’d talked for a fixed period of time, but communicating a cohesive argument in 5 minutes, with the slides advancing every 15 seconds?

It was a definite challenge and I was happy to be selected. I have no idea if every cohort works this way, but we built our talks over just 4 weeks.

Week 1: Come to a group session with only a rough topic idea (what you applied with) and talk about it for 3-7 minutes while being recorded. Feedback was given about the points that resonated for you to expand on.

Week 2: Write down your talk in 20 points and read it out loud. More feedback about timing and content.

Week 3: Put your talk in front of slides and give it to the group. I missed this one as I was out of town.

Week 4: Deliver the talk!

Of course, I practiced a lot during the weeks leading up to it. I was giving it in the shower, before I went to work, after I got home. Frankly, I was sick of it at the end.

But I’m glad I did it now.

Here’s the talk:

Functional Core, OO Shell

This video is about 30 minutes long.  Mike Gehard mentioned it to me, and I enjoyed the heck out of it.  Many takeaways from this.  One is is how using test doubles make sense at the beginning of a software project, but how it will eventually come back to bite you as the method signatures of dependencies change, and your tests don’t fail.  Another is how you can write ‘fauxO’, a nice combination that has the strengths of functional programming and OO programming.  A third is how and where integration tests make sense.

Overall, what he proposes is that the boundary of any object that makes decisions have parameters that are simple values so that that it is always easy to unit test them.  Out of this proposal fall several nice features.

Well worth the time to watch.

Re:Invent Videos

AWS Re:Invent is supposed to be a great conference.  I have thus far been unable to attend, but the videos of the presentations are posted online with about a day’s lag.  So, like most conferences, you really should be networking and meeting people face to face rather than attending the presentations.

Here’s the AWS Youtube channel where you can watch all the videos, or just sample them.

I’ve found the talks to be of varying quality.  Some just rehash the docs, but others, especially the deep dives, discuss interesting aspects of the AWS infrastructure that I haven’t found to be documented anywhere (here’s a great talk about Elastic Block Storage from 2016).  The talks by real customers also give a great viewpoint into how AWS’s offerings are actually implemented to provide business value (here’s a great talk from 2016 about using Amazon Machine Learning to predict real estate transactions).

It’s a sprawling conference, well suited to AWS’s sprawling offering, and I bet no matter what your interest, you will be able to find a video worth watching.

Announcing the Introduction to Amazon Machine Learning Video Course

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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.