Data Science Seminar Spring 2022
- Post by: Bahareh Arghavani
- November 29, 2022
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| DATE | TIME | TITLE | PRESENTER | LINK |
| 26-Feb | 12pm-1pm EST | MACHINE LEARNING IN THE REAL WORLD – LIFE CYCLE AND ENGINEERING PRACTICE | Fariborz Baghaei Naeini, Ph.D. | YouTube Presentation Slides |
| 08-April | 11:30 AM- 12:30 PM ET | Real-world Applications of machine learning in IoT and edge devices | Dr. Mo Haghighi | Zoom Link |
MACHINE LEARNING IN THE REAL WORLD – LIFE CYCLE AND ENGINEERING PRACTICE
Abstract
It has been almost a decade that deep learning has enabled researchers and engineers to tackle unsolved challenges in many fields. However, an adaptation of data-driven approaches for industrial solutions requires a significant change in the development life cycle and team mindset to achieve high-quality products. In this presentation, we will talk about the end-to-end machine learning life cycle and highlight common practices and challenges in ML-based computer vision solutions.
-Fariborz Baghaei Naeini, Ph.D.
AI Product Manager / ML Tech Lead
Real-world Applications of machine learning in IoT and edge devices
Abstract
As we continually search for bigger and more complicated problems, the demands we place on the computing power continue to grow rapidly. AI is getting embedded into billions of smart objects and IoT devices surrounding us and the environment we live in. Given the resource constraints and limited connectivity of IoT devices, only a small subset of pre-defined functions has been integrated into these devices. How can we continue to build models and algorithms that scale as our data do, or enable us to tackle even more intricate problems? For IoT devices to truly simulate human intelligence, not only do they need to implement neural networks, but they also need to take advantage of their distributed resources collectively. Most conventional IoT gateways and edge devices, however, are only capable of running basic data aggregation functions due to their limited parallel processing capabilities of their CPUs. In this talk, Dr Mo Haghighi will explore the challenges and opportunities in edge computing, as well as discussing various platforms and their applicability for various real-world scenarios. This talk will also explore using containers in IoT applications, and large scale data mining on embedded GPUs and Hybrid cloud scenarios.
Dr. Mo Haghighi
IBM