Testing Machine Learning
August 26th, 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.
Smart Automation leverages feedback loops between humans and machines to efficiently evolve systems and effectively leverage each other's strengths. Humans can think creatively and openly to find novel solutions while machines can efficiently search through enormous amounts of data to find specific patterns. When these two skill sets are combined appropriately, they provide huge value. In this post we go through a high-level view of feedback loops for Smart Automation and the best ways to leverage them.