Snapt Blog

Applications and services are the backbone of a company's digital ecosystem. They ensure that businesses run smoothly, customers are satisfied, and employees can do their jobs effectively. However, all applications have weaknesses that attackers can exploit to compromise users or steal sensitive data. In addition, every application has an expected level of performance that customers expect; if it falls short of its requirements, users will switch providers.

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.

I recently got into watches. Naturally, I became an insufferable bore almost immediately. It’s a culture and a field of engineering that comes with two centuries of jargon from hairsprings to tourbillons. If you can’t tell your tapisserie dial from your patisserie croissant, you’ll find it pretty impenetrable and exclusive.  So obviously it made me think about load balancers and other app infrastructure.

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.

This is the ultimate guide to securing your applications, servers, and APIs, including design, coding, testing, compliance, deployment, infrastructure, and threat intel.  Cyber security is of critical importance: According to a TransUnion Holiday Retail Fraud Survey, 46% of customers are concerned ...

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 previous articles, we compared high-performance development frameworks for front-end and back-end. This guide will look at the best frameworks for building fast APIs. 

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?

The introduction of containers has revolutionized the software development industry and made building, testing, and deploying applications significantly easier. However, modern applications and infrastructure require an ever-growing number of containers and servers. These generate such a volume of logs and information that monitoring everything is a massive challenge. Deadlines, inexperience, culture, and management are just some of the obstacles that can affect how successful teams are at overcoming this challenge.