Introduction Machine learning is an exciting field that can be used to solve problems, but it’s not always clear which type of machine learning a problem calls for. That’s why in this post we’ll discuss the differences between supervised and unsupervised machine learning. Supervised Learning Supervised learning is a machine […]
Month: October 2023
Introduction The term machine learning refers to a wide range of methods for extracting patterns or structures from large amounts of data. It’s often used as an umbrella term for many types of algorithms that are used in various fields, including computer science, statistics and artificial intelligence (AI). Machine learning […]
Introduction If you’re running a business, you need to be resilient. You need to keep your systems up and running when they’re needed most; you need to be prepared for unexpected disasters and outages. And if you run your business on the cloud, that resilience should extend beyond your physical […]
Introduction The promise of the Internet of Things (IoT) is that it will make our world smarter. But there are significant challenges to making this work. One of those challenges is latency. Latency is the time it takes information to travel from one place to another. For example, if you’re […]
Introduction Real time data processing is a term that’s been used in various contexts over the last few years. But what does it really mean? And why should you care about real time data processing? This blog post will help you understand the basics of real time data processing, including […]
Introduction The cloud is a network of remote computing resources that you can access over the internet. For example, your data may be stored on a server in the cloud if you use a service like Dropbox or Google Drive. Edge computing, also known as fog computing, is different from […]
Introduction Real time data processing is the ability to process and store data as it arrives, rather than later. Real-time processing can be used to generate alerts when certain conditions occur. It can also be used to detect patterns in the data. For example, a car manufacturer might want to […]
Introduction Cloud computing is becoming a popular way of getting work done. As more businesses start using cloud services, however, we’re realizing that there are some challenges with interoperability and portability in this new environment. The cloud is not one homogenous environment, but rather many different ones, each with their […]
Introduction Data visualization is a critical part of any data science project. Often, the visualizations are the only way for end users to get the most out of their data. That’s why it’s so important to have strong data visualization skills and an eye for design when you’re working on […]
Introduction It’s not uncommon for organizations to make analytics data worse. Whether it’s a result of poor record-keeping, an overabundance of data or bad processes, it happens all too often: companies gather information, then lose it. In this post, we’ll outline five steps you can take to make sure your […]