With use cases like computer vision, natural language processing, predictive modeling, and much more, deep learning (DL) provides the kinds of far-reaching applications that change the way technology can impact human existence. The possibilities are limitless, and we’ve just scratched the surface of its potential. There are three significant obstacles for you to be aware of when designing a deep learning infrastructure: scalability, customizing for each workload, and optimizing workload performance.
How Well-Designed Infrastructure Can Overcome Challenges to Big Data Analytics Workloads
October 6, 2022 by
In this sponsored post, our friends over at Silicon Mechanics discuss how using big data analytics and predictive analytics through deep learning (DL) are essential strategies to make smarter, more informed decisions and provide competitive advantages for your organization. But these tactics are not simple to execute, and they require a properly designed hardware infrastructure.
