Discover Mitra’s AWS ML Models: Part 2

UK & EUROPE 11:00 – 12:00 (GMT)

21st of February, 2019


This is the next instalment in a series of webinars on Mitra Innovation’s new Machine Learning models on Amazon Web Services (AWS) Machine Learning Marketplace. The AWS ML Marketplace was introduced in November 2018 at the AWS annual learning conference, re:Invent, which had more than 30 partners involved in the launch of the new online marketplace, including Mitra.

Having released three models as part of the marketplace launch, Mitra now has four ML models published on the AWS Marketplace including the ‘Abusive Text Detector’ model that was covered in our previous webinar. In this session will go through the other three ML models, giving you an overview of how they work, their use cases and how to subscribe to them via the AWS Marketplace.

These models include:

  • Bitcoin price predictor, which uses past price trends to help you buy and sell your cryptocurrency with increased confidence.
  • Diabetes detector, which allows you to identify whether someone is likely to be diabetic based on parameters such as age, BMI, glucose levels and blood pressure.
  • Neural style transfer model, which can combine an image with a style/design from another image to give a composite image with a styling effect. This can be used in artwork applications such as logo and icon creation or fabric design generation.

Presenter 1:

Nirojan Selvanathan – Senior Software Engineer

Nirojan Selvanathan is a Senior Software Engineer of Mitra’s Research and Development team. He is a google certified data engineer and an experienced full stack developer with 4+ years of industry experience. His interests are in cloud computing, data engineering and machine learning related domains.

Presenter 2:

Justus Nithushan – Research and Development Intern

Justus is a computer science engineering undergraduate at Jaffna University and is currently working with Mitra’s research and development team on new and exciting projects. His interests are in machine learning and data engineering.