Schedules

Event Schedule

We are in the process of finalizing the sessions. Expect more than 30 talks at the summit. Please check back this page again.
Here is our schedule from last year.

Expand All +
  • Day 1

    February 11, 2021

  • Quantum theory provides an unvarying framework for the construction of various modern physical theories. After more than 50 years from its inception, quantum theory married with computer science, another great intellectual triumph of the 20th century and the new subject of quantum computation was born. One of the fertile areas for quantum computing is AI (Artificial Intelligence), which relies on processing huge amounts of complex datasets. There is also a need to evolve algorithms to allow for better learning, reasoning and understanding.
    TECH TALKS

  • This talk underlines the necessity of a custom designed recommender system for a business case comprising of several complex underlying business rules & intertwined ways of working. It will help the audience to think out of the box when off-the-shelf machine learning algorithms don’t completely solve a business problem & warrant a custom-made solution. The services unit of Ericsson wanted to have a recommender system for their resource manager who routes an incoming project demand to an appropriate pool of resources today, based on his experience. The demand routing is considered successful only if the routed resource pool has the expected resources available who can support the delivery of the project. The standard algorithms like apriori, FP-growth etc. were not suitable for this problem because each recommendation needed to factor in the complex feature set of the input project demand & recommend out of a set of 700 pools. The input demand feature set was a mixture of several categorical, time based, text & few numeric features. The problem complexity was further compounded by the need to consider the composition of the resource pools before recommending the suitable resource pools. The AI & Data Innovation team devised a customized Machine Learning solution which was a combination of innovative feature engineering, clustering techniques, classification algorithms & usage of real time resource availability data via APIs. The automated offline training of the model using an Azure Databricks pipeline enables it for quick adaptability. The E2E machine learning solution was implemented as a web service using MLflow, Azure Kubernetes Services. The Machine Learning service’s enhancements are handled by the Azure CICD pipeline. The Machine Learning service also has a 3-tier support structure with Data Scientists at L3.
    TECH TALKS

  • Day 2

    February 12, 2021

  • The banking industry is going through major digital transformation and at the same time generating much more data than it used to be. The question is how does a bank leverage on these data into actionable insights for betterment of customer servicing, drive operational efficiency and productivity to sustain business growth. The speaker will share their point-of-views and experience on harvesting data for building smart operations utilizing advanced analytics and transforming to data driven operating model.
    TECH TALKS

  • Building a product requires expertise to prepare a framework and then applying the model to arrive at a result and then visualizing the results. It also involves a considerable effort to put in for the data pipeline. To build a web application or on a Kotlin/ Flutter application requires the skill of a full stack developer. This requires some specialized skills and my session will help the people to understand how to develop this product.
    TECH TALKS

  • - Application of DS in fintech & at PayU – customer acquisition and retention, fraud detection, risk analysis, etc - Job industry scenario and career opportunities in the field - Skills that companies such as PayU look for in a candidate - Careers in DS at PayU
    TECH TALKS

  • Day 3 - Workshops

    February 13, 2021

  • Streamlit is an open-source app framework for Machine Learning and Data Science teams. It is used to create beautiful dashboards and web apps in a very short time. And that too, in pure Python. In this workshop, we will learn how to deploy an ML model on streamlit and let users explore the following functionalities- Upload datasets Play around while doing Exploratory Data Analysis (EDA) Run ML algorithm Explore output and visualize it aesthetically We will also learn how to "beautify" our apps by adding buttons, sliders, checkbox, etc. Prerequsites - Python (Basic to Intermediate) Streamlit installed in your system (pip install streamlit)
    Workshops

  • Covers the following: Tensor Layers, Tensor Ops Model types - sequential, functional, custom Build custom layers Activation functions and optimizers Wide, deep and residual models Comparing DNN models Brief into CNN \ RNN
    Workshops

Check Last Year's Schedule

Schedule 2019

Extraordinary Speakers

Meet the best Machine Learning Practitioners & Researchers from the country.

  • Early Bird Pass

    Available till 8th Jan 2021
  • Access to all tracks & workshops
  • Access the recorded sessions later
  • Certificate of attendance provided
  • Access to online networking with attendees & speakers
  • 500 + taxes
  • Late Pass

    Available from 30th Jan 2021 Onwards
  • Access to all tracks & workshops
  • Access the recorded sessions later
  • Certificate of attendance provided
  • Access to online networking with attendees & speakers
  • 1,000 + Taxes