ActiveState Blog

This article was originally posted on Medium.

Undoubtedly, your business is run by open source languages; coders of all kinds - from Dev to QA to DevOps - have adopted open source languages. But we’ve also moved from general purpose languages that were easy to adopt to inventing languages that are more suited to niche problem spaces.

The Perl Conference is taking place this summer from Sunday, June 17 through Friday, June 22. I’ll be there presenting not just one, but four different sessions. Here’s what I’ll be speaking on.

Artificial Intelligence (AI), and more pragmatically, Machine Learning (ML) have been called the new electricity that's powering the Fourth Industrial Revolution (4IR). AI is considered to be the ability for an artificial system to make cognitive decisions without human input, whereas ML is considered to be the ability for a computerized system to improve performance of an automated task over time. While AI is still futuristic, ML has been causing huge disruptions across multiple industries over the past decade. For example:

When getting started with machine learning (ML) you need data -- and lots of it. Data really forms the foundation of your machine learning strategy and we’ll look at some of the considerations around data in machine learning. In a previous post we built a dog identification microservice in Python. We’ll consider that same use case here when looking at how we need to work with our data.

*Note: this post was originally posted on LinkedIn. 

We’re starting our own March madness, and it’s all about machine learning (ML). Want to share your ML story? Drop me a Line!

ActiveState has been betting on Python for a while. We’ve been providing companies with Python language-builds since 1999. Since before it became the language of choice for data science projects. So we get why Python would be the means to bridge the worlds of data science and engineering.