Deploying Machine Learning for Internet of Things Devices

The Internet of Things offers a great opportunity to understand machine learning and deep learning in a hands-on manner. In this session, we will build a computer vision model in the cloud and deploy the trained model on an edge device. Devices will be shared in a group. Cloud credits will be provided per group to train the model. Knowledge of Python and some machine learning knowledge is needed for this session.

Facilitators

Ajit Jaokar:

Ajit Jaokar s the course director at Oxford University for the course Artificial Intelligence: Cloud and Edge Implementations. Besides Oxford University, Ajit has also conducted AI courses in LSE, UPM and part of the Harvard Kennedy Future society research on AI.

Based in London, Ajit's work spans research, entrepreneurship and academia relating to Artificial Intelligence (AI) and Internet of Things (IoT). Ajit works as a Data Scientist through his company feynlabs - focusing on building innovative early stage AI prototypes for domains such as Cybersecurity, Robotics and Healthcare.

He is also currently working on a book to teach AI using mathematical foundations at high school levels. Ajit was recently (Oct 2017) listed in the list of top 30 influencers for IoT for 2017 along with Amazon Bosch Cisco Forrester and Gartner by the German insurance company Munich Re. Ajit publishes extensively on KDnuggets and Data Science Central and his book, Data Science for Internet of Things, is included as a course book at Stanford University. He was recently included in top 16 influencers (Data Science Central), Top 100 blogs (KDnuggets), Top 50 (IoT central), No 19 among top 50 twitter IoT influencers (IoT Institute).

Ayse Mutlu:

Ayse Mutlu is a data scientist working on Azure AI and devops technologies. Based in London, Ayse’s work involves building and deploying Machine Learning and Deep Learning models using the Microsoft Azure framework (Azure DevOps and Azure Pipelines). She enjoys coding in Python and contributing to Open Source Initiatives in Python

This workshop is held in partnership with Ripple UBRI