More accessible than ever, ML will be leveraged to reshape essential processes within the industry. Since the Industrial Revolution, manufacturing has undergone several transformations to harness new technologies as they become available. Over the past decade, advancements such as complex robotic systems, IoT technology, and artificial intelligence (AI) have pushed us into yet another round of technological adoption, the Industry 4.0 Revolution. As manufacturers embrace cutting-edge technologies, old methods are being augmented to achieve better, safer, and more profitable operations. Recently, machine learning (ML)--a sub-domain of AI that provides systems with the ability to automatically learn from experience without being explicitly programmed--has become increasingly pervasive in nearly every industry, but it's only just beginning to live up to its full potential, especially in manufacturing.

Machine learning (ML) and data science are often mentioned in the same breath – and for good reason. The two complement each other. However, understanding how they work – and work together – is important. Machine learning is a branch of artificial intelligence (AI) that empowers computers to self-learn from data and apply that learning without human intervention. Data science, on the other hand, is the discipline of data cleansing, preparation, and analysis.

In this talk, presented at Accelerate AI East 2019, Ingo Mierswa presents the ideas of understandability, transparency, and governance in machine learning, and how those pieces all work together. Ingo Mierswa is an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany. Mierswa, the scientist, has authored numerous award-winning publications about predictive analytics and big data. Mierswa, the entrepreneur, is the founder of RapidMiner. Under his leadership, RapidMiner has grown up to 300% per year over the first seven years.

In this talk, presented at Accelerate AI East 2019, Ingo Mierswa presents the ideas of understandability, transparency, and governance in machine learning, and how those pieces all work together. Ingo Mierswa is an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany. Mierswa, the scientist, has authored numerous award-winning publications about predictive analytics and big data. Mierswa, the entrepreneur, is the founder of RapidMiner. Under his leadership RapidMiner has grown up to 300% per year over the first seven years.