As enterprises got comfortable with the terms artificial intelligence, machine learning, and big data in 2017, 2018 will be the year that organizations begin architecting a roadmap for implementation.
We as people currently produce 2.5 exabytes of data per day. What is an Exabyte? A terabyte follows the gigabyte and is equal to about one trillion bytes, or 1,000 gigabytes. Next, there is the petabyte, which encompasses nearly 1,000 terabytes. Finally, we reach the exabyte. An exabyte is equal to approximately one billion gigabytes.
In a previous post, we highlighted the current state of AIand explored ways to utilize AI to improve business processes. This second installment will cover the steps needed to prepare your company for AI.
In their efforts to deploy and apply Artificial Intelligence (AI) in their organizations, many companies are facing certain challenges, lack of resources and skills being among the most significant ones. This blog series will explain what AI is and explores how it can create value for your business right now, as well as the necessary steps to take, before any AI-based solution can be deployed. We will look at several use cases and success stories from different industries that show how AI is applied successfully today.
We are living in the golden age of artificial intelligence. Smart technologies are seeping into our lives in many ways, often bringing along very beneficial and helpful services. Although the machines have not taken over just yet, we can still see the effect of artificial intelligence in our lives.
In this fourth and final installment, we leave you with the top machine learning lessons learned from a growing SaaS company, and actionable tips to prepare your company for machine learning techniques.
We’ve all heard of the possibilities of artificial intelligence, machine learning, and deep learning. There have been many situations where artificial intelligence has made a measurable impact on an organization, and there have also been situations where organizations have wasted millions of dollars on seemingly innovative technologies with no direct output.
In part 1 of this series, we defined machine learning and made the connection to enterprise architecture. In part 2, we covered the three types of machine learning algorithms. In our third installment, we will explain how to make machine learning algorithms in six steps.
In the first part of the Enterprise Architects Guide to Machine Learning series, we defined machine learning and made the connection to enterprise architecture. In this post we will cover the three types of machine learning algorithms.
“We are in the early stages of a 10-year cycle which machine learning is morphing from a lab curiosity to a rich, pervasive technology value-add.” - Phillip Harpur, Technology Analyst.
Over 90% of the data in the world today has been created in the last two years alone. The current output of data is roughly 2.5 quintillion bytes a day. As a whole, 49.8% of the population has an internet connection. On average, the US alone spits out 2,657,700 gigabytes of Internet data every minute.