Edge computing shifts processing power closer to the point of data generation in order to reduce network congestion and inactivity periods, thereby securing the maximum benefit from your data. Through this method, companies can use this information to improve the results of their business operations. It is important to note, however, that edge computing does not provide a quick fix for all business outcomes.
Businesses must account for the constant growth of data.
Applications at the edge continue to generate massive amounts of information, and often, organizations need to make decisions in real time based on that data. A key approach to doing this is through the implementation of artificial intelligence (AI) and machine learning (ML). AI and ML are allowing companies to make sense of their data and extract the maximum value out of their assets, while also speeding up the push to the edge.
A machine learning service can help achieve your business goals and fuel your digital transformation through improvements in customer experiences, employee productivity, cost reduction, and fraud reduction. The problem is that determining where to start when applying machine learning can be challenging. You can eliminate this barrier for your organization by implementing practical and proven machine learning tools that can quickly translate into real business benefits.