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Machine learning operations (MLOps) is a field in machine learning (ML) that is focused on automating the deployment, monitoring and managing of ML models that are moved into production, allowing it to be scalable so that business value can be created. Different teams within an organization (Data Scientists, DevOps engineers etc.), processes, practices and technologies are integrated to build MLOps pipelines.

As the world becomes more digitally focused with mobile applications increasing in popularity, there is a significant demand for software tools that can be used to build cross-platform applications quickly and effectively. Regardless of the operating system running on your device, a mobile application should provide you with a seamless user experience. This is where Flutter comes in.

“The cloud” has become a buzzword over the years, especially within the business space. So what exactly is it? The cloud is a collection of data centers that are located all over the world. These data centers consist of servers that are accessed by users over the internet, and provide users with access to software and data. The cloud exists through virtualization, which is the process whereby physical computers, otherwise known as virtual machines, are mimicked using software. Many users can interact with these virtual machines at one time, where each virtual machine remains autonomous and doesn’t interact with the others.

In a company, it’s extremely important that everyone works together to contribute to constantly bettering the company and customer experiences. A team of data engineers, data scientists and business analysts can work together to extract meaningful insights from raw data that is collected over time. These insights can provide a company with a competitive advantage and assistance in making informed business decisions. Overtime misconceptions have arisen around the roles that data scientists and data engineers play in industry. In many companies, both roles are thought to be interchangeable, however, they are distinctly different. Let’s have a look at the differences!

Great, you’re sorted with Machine Learning and Artificial Intelligence and you’re good to go, right? Wrong. Now you need to think about data collection, algorithms and all the rest of it! Don’t worry, we’re here to help you out! In machine learning (ML) and artificial intelligence (AI), both of which we have covered in previous blogs, there are two main ways that an algorithm can make observations in a dataset, that being through supervised or unsupervised learning. Today we’ll have a look at what the differences are between the two, as well as which would be better for YOU. Let’s get to it.

As customer service and support becomes more and more important, companies are having to come up with new ideas to keep their customers happy and to respond to their requests or questions as soon and as conveniently as possible. Cue Chatbots – as messaging apps become a popular communication channel, businesses have started using Chatbots to interact with their customers and their employees.

Picture this – a robot or machine that can tidy your clothes cupboard JUST the way you like it, or serve every member of your house their ideal beverage specific to them, how awesome is that?! This would make your life so much easier right? Well, these are products of Artificial intelligence (AI).

Which frontend framework should you be looking at in 2021? With many options and opinions available, today we’re taking a look at the two front runners, two words you may be hearing quite frequently – Angular vs React, whats the verdict? Let’s break it down, compare the two and find out!

SQL – it’s like the database whisperer! Back in the day companies stored their records and documents in file cabinets, for large businesses this meant tons and tons of filing cabinets to accommodate all the documents. Now days we have the luxury of storing data in digital databases, although finding information within these digitised databases is not as simple as opening a filing cabinet drawer. For searching databases, Structured Query Language plays a massively important role!