What you will learn:
Here is an overview of key autonomous technology used in manufacturing, as well as use cases and examples of each one within the manufacturing space.
Autonomous technologies are changing manufacturing.
For executives determining which advanced technologies best meet their company’s needs and goals, they need answers that they can develop into strategy.
Gaining a better understanding of what each autonomous technology is and how they empower your workforce can bring more value than simply knowing which tools to bring in. This knowledge can also give you insight into infrastructure needs and whether or not your digital transformation choices support autonomous options.
This white paper:
- defines what autonomous technologies are
- explains how they fit into manufacturing
- provides examples of how autonomous technologies empower workers and move your organization forward.
Complete the form to download this white paper on autonomous technology in manufacturing.
Key differences between AI, Machine Learning, and Data Science
As with most digital innovations, new technology warrants confusion. While AI, ML, and DS are all closely interconnected, each has a distinct purpose and functionality—especially within industry.
Understand the key differences between AI, machine learning, and data science for manufacturing. Plus, see how they work together with a real use case.
Guide to Machine Learning in manufacturing
The connected factory enables manufacturers to collect, analyze, and innovate from their data. Introducing Machine Learning to this equation can accelerate the detection, optimization, and innovation capabilities of your workforce.
Learn more about what Machine Learning is and how it can be integrated into your workforce in this comprehensive guide.
Do you need to be a Data Scientist to use Machine Learning?
With the increasing amount of connected devices and easy-to-use technology, more workers are enabled to leverage this data to make informed decisions.
Still, this increase in analytics-driven decision-making begs the question: do you need to be a Data Scientist to leverage the power of augmented analytics and Machine Learning?