IT - IoT

Author Michael Roshak explores the difficulties of AI design for IoT, the considerations organizations must know about the process and what is exciting about AI and IoT

"Even the best electrical engineers and IoT practitioners might not be able to figure out the AI component of IoT artificial intelligence without some guidance.

IoT practitioners and data scientists who want to build IoT-based AI don't have to work it out on their own. In fact, they must often partner with other experts or else they risk missing-critical factors to ensure their project succeeds.

In Artificial Intelligence for IoT Cookbook, author and senior staff enterprise architect Michael Roshak discusses techniques with detailed instructions to build AI for IoT deployments and resolve common problems. After establishing the basic set up for IoT and AI, Roshak digs into advanced skills such as computer vision and natural language processing..."

If you think regular IT project managers can run IoT projects, you might be miscalculating. Here's why.

"In June 2021, the State of Indiana rolled out a project management framework that was specifically designed for the Internet of Things. Other IoT PM programs are likely to follow because running an IoT project is different than running a standard IT project, and new skills must be learned.

Like IT projects, IoT projects demand managers who have excellent soft skills (i.e. communications, collaboration, negotiation, empathy, etc.), and the ability to manage a multi-disciplinary team through a series of tasks in order to achieve an overall objective. The project manager also tends to be a task-oriented individual who never loses track of the critical path to project success, and who has the ability to shift strategy in order to circumvent the project roadblocks that inevitably arise..."

To get the most value from any industrial process, organizations must have the technology to collect IoT data, analyze that data with AI and do so in real time

"AI's growing sophistication means that organizations of any size can much more easily use AI to solve critical, complex problems.

AI has been pivotal in navigating the particularly challenging landscape of the past year. Retailers have relied on AI to help them optimize their order shipments, reimagine their stores as distribution centers and ensure people can still get products even when in-person shopping ground to a halt. In the utility sector, AI has been increasingly deployed to keep power grids running by managing issues such as vegetation risk or getting an earlier start preparing for adverse weather events. AI is also helping build better from the ground up. For example, AI analyzes and tracks requirements for complex engineering equipment used in can't-fail scenarios such as airplanes, ventilators and space shuttles..."

IoT practitioners must follow clear steps to implement an AI analytics process if they want to create an AI application with IoT that improves their deployment's performance

"If organizations want to develop AI applications for IoT deployments, they don't necessarily need to know AI theory. They must consider the many components that create cohesive foundations for AI and IoT and implement them step by step.

With the rapid adoption of IoT, AI applications have made smart devices more practical for process optimizations. IT professionals cannot process the massive amount of IoT data that sensors create in a timely manner when organizations have thousands of devices. Instead, organizations can invest in AI for IoT..."

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