A few weeks ago, a blog post titled DevOps: The Next Chapter went up on the GoCD blog. It presented a broad view of the relationship between DevOps and machine learning (ML). The post was mostly written by Barry Hawkins, who is currently a senior software engineer at HashiCorp. He has also worked on the GoCD project and is a contributor to Pachyderm, an ML-focused open-source project.
I was fascinated by the post because it showed how ML can be used in a way that’s easy to understand. It doesn’t involve setting up Kubernetes or diving into deep learning concepts. Instead, it’s about giving developers more power to make decisions without necessarily involving data scientists or machine learning experts.
In this post, I’ll unpack some of the ideas from Barry’s blog post and delve deeper into its concepts. I’ll also go into some code examples that demonstrate how ML can be applied in DevOps settings.
What Is ML in DevOps?
“The only thing that I’d rather own than Windows is English, because then I could charge you two hundred and forty-nine dollars for the right to speak it.” – Scott McNealy, SUN Co-founder
I love this quote from Scott McNealy. It’s both insightful, provocative and witty. I guess some people would be offended by it, but I appreciate it.
It also reminds me of Amazon and their Honeycode service. They’re charging companies 249$/month for a product that allows you to code apps in a spreadsheet. It’s a no-code platform.
Is this a good thing? Should we be worried about the future of software development? Or can we just rely on programming languages being harder to learn than spreadsheets?
In this article, I’ll try to answer these questions and explain how NLP machine learning in DevOps relates to all of this.
NLP is a very powerful tool in data science. It can be used to extract information from large bodies of text based on patterns and relationships between words and phrases. Machine learning is an algorithm that can help us to extract such patterns by training it with data. The problem is that NLP is not well known or understood by many people, even the most experienced data scientists.
In this post I will explain how we can use machine learning algorithms for NLP using Python. There are many different types of ML algorithms, but here I will focus on the main ones. This post assumes you have some knowledge about Python language and that you have some experience in using libraries like numpy, scipy, pandas etc. If not then please read my other posts first before continuing with this one.
What Is Natural Language Processing?
NLP stands for Natural Language Processing. This term refers to any technology that extracts information from text in a way similar to how humans do it themselves: by understanding what it means and how it relates to other things around them (other words, phrases etc.).
In order to understand how we can use NLP for machine learning, let’s first understand what machine learning actually is: A set of algorithms used by computers to learn from data without
The most important thing about a person’s name is that it is unique, and so one of the challenges in making sense of names is to find a way to represent them unambiguously. In this article we’ll look at techniques for doing just that, using techniques drawn from natural language processing (NLP) and statistics. Specifically, we’ll see how to extract features from each name, and use those features to classify names by gender.
NLP, or Natural Language Processing, has been in the news a lot recently. It’s also been making big waves in the world of business and enterprise. But what is it? How can it help you? And why should you care?
NLP, in a very simple way, is the technology behind chatbots. It’s the ability to allow a computer to understand what you mean when you say something. It sounds complicated, right? Well it is! But let me explain.
The classic example of NLP is this: You tell your phone to “call mum”. The phone recognises mum as your mother from your address book, and then calls her number for you.
NLP isn’t just about speech recognition though – that’s more voice recognition than NLP. NLP actually goes much deeper than that. For example, if I had written this blog post using Google Docs on my desktop computer and then shared that with my phone, so that I could continue writing on the move; if I had written: “I’m going to meet my friend for coffee”, Google would have recognised “friend” as a person from my contacts list (as well as “coffee” as somewhere I’ve been before), and would have suggested “Cathy
DevOps is a portmanteau of development and operations. It’s a concept that emphasizes collaboration between software developers and IT operations, with a focus on automating processes, while an agile approach is used to deliver features more rapidly and reliably.
If you’ve ever worked in a tech company, you probably will have worked on a DevOps team, or at least heard the term. The role is certainly one of the hottest jobs in tech right now! But what exactly does it entail?
DevOps teams are usually responsible for three main areas: (1) building and maintaining the infrastructure for applications; (2) deploying and operating new software releases; (3) monitoring and troubleshooting issues within the application.
We all know it can be challenging to deploy software releases in a timely manner. This can be especially true when there are many people involved across different teams! The DevOps engineer helps to streamline this process by automating repetitive tasks, ensuring code quality, managing dependencies and ensuring everything runs smoothly before deployment.
Once the application is deployed, the DevOps engineer focuses on monitoring its performance, identifying any bugs and fixing them as soon as possible. They also work with other teams to investigate when things go wrong during production and help to prevent