ActiveState Blog

*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.

I joined ActiveState last year. It’s the first time I’m working directly in the open source space. And along with learning a lot, it’s been remarkable to consider not only what the company has achieved but also the proliferation of open source software. I’ve been surprised to learn how many of us take open source for granted, especially since how it enables most of our day-to-day tech.

Organizations are focused on releasing software faster to stay ahead of the competition. Software development processes have become more flexible, push functionality out more often and in smaller chunks. In other words, organizations have adopted lean and agile principles across the software development lifecycle to move at a faster clip. But this alone wasn’t enough to move at the pace required. In order to bridge the gap between software development and the actual production operations of the code, a new approach was necessary: DevOps; a shifting left of operations.

How do you:

• ensure you don’t join the trash heap of hacked enterprises? 
• make security part of the SDLC from the get-go and not an afterthought?
• address the latest attacks that bypass traditional network security protections?

You need to take a paradigm shift in your application security. 

Organizations have been moving security controls leftward in the software development lifecycle. Now it’s time to consider baking security in from the start, before a single line of code is written. 

In my last post I surveyed the growing array of options for deploying your ML models into production. In this post, we’ll create a demo to see how simple it is to develop your own service using Python’s Flask library.

There are a number of cases where you might not be able to use a cloud service to host your model and would be required to roll-your-own inference service. In many large enterprises, on-premise solutions are mandatory. Approvals to purchase third-party solutions can also be lengthy and complex. So developing your own small service may be the best solution.