Organizations that use and scale deep learning are growing, and it is increasing the need for an inference acceleration platform that works.
As AI developers and data scientists, you work hard collecting, cleaning, and recycling data, and building models. But you hit a roadblock: The technical aspects of taking models into production that’s inherently complex. Thus, there’s now a widening gap between the available computational abilities and demand for algorithms that make deep learning affordable and accessible for everyone. How can an inference acceleration platform solve this?
Optimizing Your Deep Learning Inference Platform
Ben Lorica, the host of The Data Exchange Podcast, recently chatted with our co-founders Yonatan Geifman and Ran El-Yaniv about the benefits of optimizing your deep learning inference platform.
From discussing the latest academic research to addressing the deep learning inference challenge that companies face, listen to find out the answers to the following questions and more:
- What do hardware and software working together to get the best deep learning production results mean?
- As companies scale deep learning, what is the solution for the growing demand for more computing power and efficient algorithms?
- How can you accelerate and scale the inference of your deep learning models to meet the requirements of your use case?
- With deep learning’s energy consumption problem, what would the future be like?
- Finally, what are the steps that you should take to get started optimizing your inference strategy?
You can access the podcast episode on The Data Exchange Media website here, and let us know what you think!
