Smart Automation Can Be Transparent
August 17th, 2020
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Some people think that Machine Learning will not scale enough to meet their needs. That's incorrect. As with almost any other decision-making software, Machine Learning can scale as much as needed given the right understanding of business and system constraints, and the right design. First, how much do you really need to scale? Second, what are your success metrics? In this post we go through an example of how to make a system scale to large global workloads. There's a matching course in which we show how to implement this system.
Digital transformations are composed of many small changes in a company's operations which combined produce a whole new way of working. They empower teams with tools and skills from Systems Thinking, Machine Learning, Operations Research, and other disciplines, so that they make smarter and faster decisions by leveraging Smart Automation with data-driven algorithms. To ensure success, these transformations require diversified portfolio strategies and this post we show how to build them.