What is Industrial Machine Learning?
Data has become a corporate priority for many large enterprises. According to research firm Frost & Sullivan, more than half of Fortune 1000 firms report having big data initiatives in place across the enterprise.
The true value of these initiatives is the ability to obtain rapid insights and implement relevant changes that drive benefit. However, as the volume of data grows (Frost & Sullivan projects global data traffic to cross 100 zettabytes annually by 2025), it becomes more difficult for businesses to extract meaningful insight.
Despite making large investments in technology to store, analyze, report and visualize data, many enterprises have not seen a return on their investment. They spend too much time manually interpreting and reporting results and too much money hiring personnel that can’t completely meet the demand.
Industrial Machine Learning
The goal is to find a way to consistently produce data-driven insights at enterprise scale. This can be done with industrial machine learning (IML), which provides a scalable solution for ingesting data, building algorithms, deploying them into production, and generating continuous insights to ongoing business problems.
A method with a modern twist
IML is a modern take on a very old idea: the scientific method.
Data scientists start with a hypothesis and collect data that could be useful in evaluating the hypothesis. They then generate a model and use it to explain the data. They evaluate the credibility of the model based on how well it explains the data observed so far, and how well it explains new data that will be collected in the future. When it comes to discovering insights, this method works consistently well.
The modern twist comes in by using digital infrastructure that allows this method to be done on an enterprise scale. The evidence becomes a continuous pipeline of data being collected; the models are business algorithms running in production; and the experiments are done in very short sprints that force data scientists to focus on discovering insights in small, meaningful chunks.
By combining data science and the scale of digital infrastructure, IML can help generate those kinds of insights.
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