Articles : Craig Risi

So, you have a web application, and you are considering exploring some machine learning (ML) solutions to see where they can be of value for your business. Well, the industry has already seen a lot of development and research in this space, so there are already many successful use cases that we can look at to see how ML might benefit your particular application.

Implementing a machine learning (ML) model in an application is only a small part of the journey. When you’ve done all the research, determined that ML is a good fit for your use case, and chosen the correct ML model and tooling, you still need to ensure that you handle your data and training correctly.

In previous articles, we looked at how machine learning (ML) works and how to choose the right learning model for you. However, just because we can use machine learning, it doesn’t follow that it is always the best solution for any computational problem. So, in this guide, we will look at some of the limitations of ML, when to use it, and when to avoid it.

In a previous article, we introduced machine learning and how it works. You can choose from a wide array of machine learning (ML) algorithms, but which model is best for solving your particular problem?

In a previous article, we provided an introduction to artificial intelligence (AI) and the different learning types. Machine learning (ML) is one of the biggest types of AI development—and the one that gets the most attention from many companies.

Every business exposing online applications, services, and APIs needs a Web Application Firewall (WAF) to operate safely and protect against cybersecurity threats. A WAF identifies and blocks attacks that lead to downtime, leaked data, and compromised transactions and accounts. However, you might find that choosing the right WAF is a tricky business. 

Designing for scale is one of the fundamental principles of modern software engineering. However, an organization’s ambition to build scalable applications often exceeds its ability. While it's easy to see the incredible opportunities for scaling up and going global, many do not appreciate the biggest challenges facing development and DevOps teams building scalable apps.

We previously addressed the need to design secure applications and test for security during development. However, despite all good intentions, it is all too easy to focus on solving the immediate functional problem and ignore other objectives like security when writing code. 

We constantly read about leaks and security attacks that hit well-known applications. Businesses cannot take security for granted. With so much critical data in play, they must prioritize application security and the process of identifying security flaws to ensure apps are safe.