The global pandemic has paused or inhibited momentum for a number of industries, but not the trucking industry. The economy relies heavily on drivers and fleets. Although it faces various obstacles -- including increased demand and fewer drivers to meet it – the trucking industry continues to see significant growth. According to the American Trucking Association, the trucking industry was short roughly 60,800 drivers back in 2018, and the shortage has continued. Experts estimate 80,000 fewer available drivers on the roads in 2021 vs. a year ago.

Today, machine learning (ML), artificial intelligence (AI) and decision optimization are not just buzzwords found all over the news. They are urgent requirements for many companies that fear disruption, want to perform pragmatic analysis and make better decisions with their data. Data has been called the next natural resource, like oil. But just as with oil, it must be refined to be valuable, and its end value must exceed the cost of refining it. With data, the value of AI is the cost of investing in collecting, organizing and analyzing all that data.

WAN optimization is one of the most commonly put forward ways in machine learning could be applied to SD-WAN. In theory, machine learning would, perhaps using some form of topology optimization, be able to analyse network branch traffic and work out the most appropriate connection for traffic of its type available at the time. Machine learning algorithms could then also be used to automatically route the traffic down the selected connection. There are currently models of WAN optimization technologies being worked on that shift traffic while taking into account security requirements, costs, and the application's attributes. It is hoped that, as machine learning and WAN path optimization technologies are refined and improved, they will eventually lead to increasingly intelligent and automated traffic optimization becoming a standard within SD-WAN solutions.

"What is'machine learning?" is high on the list of most-commonly-asked questions of the Sidecar team. Machine learning underpins Sidecar's optimization technology for product listing ads, and we're not shy about making that fact known. But we understand that the term might sound a little bit sci-fi to some. However, since it's so central to what we do and who we are, we thought a simple explanation of machine learning was in order -- no computer science degree required. At its simplest, machine learning means giving computers the power to teach themselves to make decisions using historical examples, rather than explicitly programming them to perform a task.

Machine learning underpins Sidecar's optimization technology for product listing ads, and we're not shy about making that fact known. But we understand that the term might sound a little bit sci-fi to some. However, since it's so central to what we do and who we are, we thought a simple explanation of machine learning was in order -- no computer science degree required. At its simplest, machine learning means giving computers the power to teach themselves to make decisions using historical examples, rather than explicitly programming them to perform a task. To illustrate this concept, I'm going to borrow an example from The Data Skeptic, a popular -- and highly recommended -- podcast by Kyle Polich.