Webinar: "Machine Learning and Deep Learning on Edge Devices using Tensorflow"

To access this webinar, please register here: https://aiplus.odsc.com/courses/machine-learning-and-deep-learning-on-edge-devices-using-tensorflow

Topic: Machine Learning and Deep Learning on Edge Devices using Tensorflow

Speaker: Aditya Bhattacharya, Lead AI/ML Engineer at West Pharmaceutical
https://www.linkedin.com/in/aditya-bhattacharya-b59155b6/

Bio:
Aditya is currently working as the Lead AI/ML Engineer at West Pharmaceuticals and previously worked in Microsoft as Azure Cloud Platform development engineer. He is well seasoned in the
fields of Machine Learning, Deep Learning, Internet of Things (IoT), Robotics and Cloud Computing. Apart from his day job, he is an AI Researcher at MUST Research, and one of the faculty members for
the MUST Research Academy. Aditya is passionate about contributing to the open-source community. His contributions for the community can be found on his website: https://aditya-bhattacharya.net/, in Medium: https://medium.com/@adib0073 and in GitHub :
https://github.com/adib0073 and has delivered sessions in reputed forums and events like ODSC India, Indo Data Week, Data-Driven Investor.

Abstract:
By 2025, it is estimated that there will be 21 Billion IoT devices in the entire world. With the advancement of artificial intelligence and IoT, there will be a huge demand of Machine Learning and Deep Learning applications at Edge or Node devices. So the question is, how exactly can we perform real-time machine learning on the edge? In this session, we will take a tour of suite of approaches powered by Tensorflow, that can help us to develop real-time machine learning and deep learning
operations on edge devices!

Key Takeaways:

1. Training and inferencing of ML and DL models on client browsers and edge devices using Tensor.js and Node.js
2. Using Tensorflow lite for training and deployment ML and DL models on light-weight edge devices like Raspberry Pi.
3. TensorHub for registering and versioning trained models that can be deployed on edge devices.
4. Case-studies where real-time model training and inferencing are required for light-weight edge devices.

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