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Deep Learning in Production Meetup

  • Havana, 5th Floor, CIC Cambridge One Broadway Cambridge, MA 02142 (map)

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Machine Learning Inference on Mobile Devices 
A lot of the cool new features that we love on our phones like face recognition in photos, Face-ID for authentication, smart reply in Gmail etc. rely on Machine Learning, most of which takes place on device. But, it can be really challenging to deploy these Machine Learning models on edge devices like Mobile Phones, embedded processors etc. 

In this talk, we will discuss that why we should look at doing Machine Learning inference locally on the mobile devices and also explore that what kind of an expertise is required for a developer to deploy a Machine Learning model on a Mobile Device. 

We will also develop and train some Machine Learning models for different use cases, deploy the trained models in an Android/iOS App and see that how we can leverage the power of the CPU/GPU/AI Chips on our Mobile Phones/embedded devices etc. for doing inference locally. 

BIO: Anuj is a Machine Learning Researcher at Bose Corporation. His work involves exploring, innovating and developing new machine learning models to create solutions that will amaze the customers. 

Apart from that, his work also involves optimizing and deploying machine learning models on mobile devices [iOS/Android], embedded devices like ARM Cortex A/Cortex M processors as well as on Blockchains for quick proof-of-concept development. 

Anuj is also a contributor to the OpenMined project, a project to develop tools for secure, privacy- preserving, value-aligned Artificial Intelligence. 

Requirements and Resource Links: 
You do not need to code along. All the code for the Machine Learning models, iOS and Android App code as well as the presentation for this talk will be available after the talk on GitHub []. 

Deep Learning for NLP at HubSpot 
BIO: Vedant was founder and CEO of Kemvi, acquired by HubSpot (NYSE:HUBS), where he now works on deep learning for NLP at the intersection of research, engineering, and product. Kemvi leveraged novel models and a distributed web-scale pipeline for information extraction to personalize messaging at scale and accelerate sales and marketing teams. Vedant studied physics and math at Columbia University and has publications and patents across machine learning, human computer interaction, theoretical black hole physics, and quantitative finance. His work has been covered by TechCrunch, Fortune, Wired, Technology Review, and others. 

Deep Learning on Mobile Devices 
Machine learning and AI algorithms now outperform humans on tasks ranging from image recognition to language translation. However, sending video, audio, and other sensor data up to the cloud and back is too slow for apps like Snapchat, features like “Hey, Siri!”, and autonomous machines like self-driving cars. Developers seeking to provide seamless user experiences must now move their models down to devices on the edge of the network where they can run faster, at lower cost, and with greater privacy. This talk covers the reasons developers should be deploying deep learning models to edge devices, common roadblocks they will face, and how to overcome them. 

BIO: Dr. Jameson Toole is the CEO and cofounder of Fritz—helping developers teach devices how to see, hear, sense, and think. He holds undergraduate degrees in Physics, Economics, and Applied Mathematics from the University of Michigan as well as an M.S. and PhD in Engineering Systems from MIT. His work in the Human Mobility and Networks Lab centered on applications of big data and machine learning to urban and transportation planning. Prior to founding Fritz, he built analytics pipelines for Google[X]’s Project Wing and ran the data science team at Jana Mobile, a Boston technology startup.