I am a Junior Year Student at Sathyabama IST currently undertaking my Bachelors in Computer Science and Engineering. I am an Intern at Red Hat with the CCS Team. I am also working with moja global as a Technical Writer under the Google Season of Docs 2021 Open-Source program.
I'm a Software Developer with experience in Development, Analysis and Content Writing with particular expertise in Web, Machine Learning and Cloud Technologies. I have been an MLH Fellow for the Fall '2020 Batch, a Former Software Engineering Intern at Qxf2 Services and a Summer Intern at Scholify.
Cancelled Talks:
Build interactive Python dashboards using ExplainerDashBoard
With the rise of machine learning and data visualization, engineers always feel the need for a better way to visualize and interpret data. The particular use-case of having data visualization varies a lot. It can range from the need to have an interactive dashboard to analyze the data models to building real-time dashboards to observe the data metrics and performance. Building dashboards requires intensive front-end engineering with the technical specifications being much varied compared to the static graphs.
In this talk, I will be talking about ExplainerDashBoard, a highly customizable Python package that allows us to make dashboards in less than ten lines of code. Powered by interactive Dash components, Plotly graphs and a Flask backend it allows us to develop interactive dashboards. and customize them.
The talk would help you make a simple Machine Learning model and visualize it with the aid of a dashboard built using ExplainerDashBoard. The overall agenda of the talk includes:
- Need for Dashboards in Data Modelling
- Existing solutions and the problems with them
- Introduction to ExplainerDashBoard
- Making a Dashboard with ExplainerDashBoard
- Deploying our Dashboard on Cloud
- Q&A
You can know more about the ExplainerDashboard here: https://github.com/oegedijk/explainerdashboard
By the end of the talk, we will learn how you can build your custom dashboard for your machine learning model. We will also go forward and deploy the whole dashboard as a Web Application on a free-tier Heroku server.
