Tech Talks

Most AI projects do not reach beyond the pilot phase. Because, data driven decision making needs to have clarity of process which deep learning or complex models do not allow for. Thus, it’s high time for a focussed work flow for explaining AI and black box models. I will talk about Explanability and Interpretability of deep learning models using DeepLift and the latest algorithms for XAI, which have come a long way from Variable Importance and Shapley Values. This will help deep learning practitioners to understand and communicate models with their business partners.

Formula E has gained popularity as a sustainability-conscious sport that originates innovations to improve electric vehicles. The premise behind Formula E, is not only that the cars are fully electric, but that the 11 teams, each with two drivers, compete in identically set-up, electric battery-powered race cars. Rich within each season, performance data on racing dynamics, the driver and the car itself across the past seven seasons are excellent sources to provide a reliable basis for any forecasting/simulation using state of the art optimization and data science algorithms.

With rich sources of available data ranging from past driver performances, their lap times, standings in past races, weather, and car technical information such as battery, tires, engine; data scientists can predict the number of laps a car can complete by quantifying behavioural attributes such as driver’s risk-taking appetite, and other traits such as track information, weather which can affect the races. The purpose of this exercise is to define the approach to use historical data to predict the number of laps a car would finish in 45min for an upcoming race. We built an ensemble model with combination of an intuitive mathematical model and an instinctive deep learning model to predict the number of laps at the end of every race.

Computer Vision is the field of AI that helps us understand visual data such as pictures and videos using advanced algorithms. Till late 1990s, people mainly used hand-crafted features for Computer Vision tasks and such methods were complex and inefficient. Neural Networks were used in late 1990s but were not very successful due to lack of good training data and limited computing power. In 2009, Imagenet provided large labeled training data and started a revolution in the Computer Vision field. Application of Convolutional Neural Network (CNN) gave impressive results in this area. Alexnet, Resnet and Yolo are some breakthrough models that led to massive progress in the field. Deep Learning has been widely used in image classification, object detection, and image segmentation tasks. Computer Vision plays a critical role in self-driving cars. Generative Adversarial Network (GAN) is an exciting development in Deep Learning that can be used to create realistic artificial images and videos

In machine learning – we are accustomed to traditional ways of representing data, where we assume a set of i.i.d data points represented by tabular features and we try to learn a statistical model on this dataset. In real life, information flows through networks of interacting entities, and individual data points are interconnected through rich, informative relationships. Imagine the data representing social networks, search engines, geospatial systems, question answering systems, protein interaction networks, even molecular structures. Graphs are a natural choice to represent these use cases. We connect data points (entities/nodes) with relationships(edges) and optionally embed each node and edge with features. Where traditional machine learning algorithms fail to exploit these inductive biases, graph neural networks (deep neural networks that operate on graphs) have been successfully applied and are seeing an increase in adoption by the industry. This talk will cover how to model datasets as graphs and using GNN to solve use-cases like node classification, link prediction, clustering, and learning high-quality embeddings.

Data scientists need current, clean consolidated data sets to unlock the potential of machine learning. However, they often face challenges due to infrastructure constraints and data that is siloed and outdated. A data strategy with a scalable, real-time cloud data platform as a central pillar, is key to address these challenges. In this session, experts Shaji Thomas and Swagata Maiti from Ugam, a Merkle company, will deep-dive into 7 techniques which will help you build a scalable data platform. These techniques include automated data validation, reusable feature stores, streaming ingestion, transformation of IoT sensor data and more.

With privacy being the buzzword in data collection and analysis, how should the tech world be prepared for a differentially private world?

This talk will be about our recent work on using deep learning techniques to predict credit ratings for global corporate entities; obligations are compared to most popular, traditional machine-learning approaches such as linear models and tree-based classifiers. The results of this work also demonstrated an adequate accuracy over different rating classes when applying categorical embeddings to artificial neural networks (ANN) architectures.

This talk is an insightful discussion of my Deep Learning AutoML library, Deep AutoViML. deep_autoviml is a powerful new deep learning library with a very simple design goal: Make it as easy as possible for novices and experts alike to experiment with and build tensorflow.keras preprocessing pipelines and models in as few lines of code as possible.

Agenda of the talk includes:

1. Top features of the library.
2. How the library can be used in the context of MLOps.