Understand the key differences between AI, machine learning, and data science for manufacturing. Plus, learn how they work together with a real use case.
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Type: Articles
Key Considerations for Scaling Your IIoT Platform
Scaling your IIoT platform will likely come with hurdles. Here’s some recommended considerations for scaling your IIoT platform successfully.
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Unexpected Use of AI During COVID-19
From a sudden freeze on the economy to returning to limited or scaled production, AI has played an integral part in the world of manufacturing. There are different ways AI is leveraged in Industry.
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Five Surprising Considerations for Your IIoT Implementation
While there are plenty of examples of “best practices” for your IIoT implementation, these five less-obvious approaches may be the key to helping your implementation go smoothly.
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Do you Need to be a Data Scientist to Use Machine Learning?
The smart factory is no longer a thing of the future. For many manufacturers, it is the reality now. The growth of augmented analytics and Machine Learning in manufacturing has made way for a new Data Scientist in the factory.
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4 Ways to Use Machine Learning in Manufacturing
Continuing to find new ways to improve operations requires increased creativity, capacity, and access to critical data. Industrials use Machine Learning to identify opportunities to improve OEE at any phase of the manufacturing process.
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A New Start-Up Strategy: Resilient Growth
We are currently living in an exceptional situation. We are suffering the consequences of unexpected circumstances that took us by surprise. But like most entrepreneurs, I believe we should be building the future rather than predicting it.
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The Importance of Legitimizing Artificial Intelligence
Companies go to great lengths to protect their data. Now the time has come to protect ourselves from using data poorly or inaccurately. Without legitimacy, AI is a real danger.
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IIoT Platform: Cloud or Edge?
Often, companies seek a cloud or edge solution in their pursuit of Industry 4.0. The solution is not either or: it is based on your goals.
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Data Scientists Are in the New Professional Bubble
In this article, you’ll see some of the mistakes that organizations make when implementing their digitalization journey. Learn about the challenges manufacturers have transforming their data and what that means for the future of industry.
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