Submit a talk on data
Submit talks on data engineering, data science, machine learning, big data and analytics through the year – 2019
Accepting submissions till 31 Dec 2020, 11:59 PM
Not accepting submissions
Submit a talk on data.
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AT
Akash Tandon Making sense of messy data to track disease outbreaks in IndiaIn spite of open data portals cropping up across multiple domains, working with the datasets they provide is difficult. In our bid to identify disease outbreaks and aid preventive health-care, we came across one such data source. more
Technical level: Intermediate
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AP
Aravind Putrevu A NoSQL search engine to search ^ H ^ H ^ H ^ H ^ H ^ H find ...Are you still searching within your data with some SELECT * FROM person WHERE name like ‘%aravind%putrevu%’? more
Technical level: Beginner
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AP
Aravind Putrevu Machine Learning with ElasticsearchAs volumes of data increase, manually searching and visualising consumer or user behaviours becomes more and more difficult. An alternative approach is to use machine learning to automatically build behavioural models of these behaviours. more
Technical level: Intermediate
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KD
Kalpit Desai  The Catalog as a Catalyst - Bringing benefits of Big Data to MSMEsWhile large enterprises have the necessary resources to acquire and process Big Data, the Micro / Small / Medium enterprises in emerging economies like India are far from being ‘data-driven’. This is a huge opportunity untapped, considering that MSMEs account for more than 99% of businesses, and they make up the backbone of our economy. For the opportunity to be leveraged, a crucial pre-requisite… more
Technical level: Advanced
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KD
Kalpit Desai The Catalog as a Catalyst - Bringing benefits of Big Data to MSMEsWhile large enterprises have the necessary resources to acquire and process Big Data, the Micro / Small / Medium enterprises in emerging economies like India are far from being ‘data-driven’. This is a huge opportunity untapped, considering that MSMEs account for more than 99% of businesses, and they make up the backbone of our economy. For the opportunity to be leveraged, a crucial pre-requisite… more
Technical level: Advanced
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US
Upendra Singh  How organizations can leverage 'Large Scale Graph Based Analytics’ to derive value from their data.An organization’s data is like a living organism - growing, expanding and evolving over time to form complicated and connected systems. This is similar to biological evolution, where life forms evolved from simple unicellular structures to more and more complex multicellular organisms. And as organizations compile more and more data, it is crucial for them to understand that the value of any data… more
Technical level: Advanced
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VA
Vamsi A Preparing AWS instances for performance and resource intensive applicationThe ASR module of Samsung’s Bixby does the Speech to Text translation. The accuracy of the output depends heavily on the quality of models involved. more
Technical level: Beginner
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AC
Abhijith Chandraprabhu Introductory workshop on Computational Machine LearningYou have been hearing about machine learning (ML) and artificial intelligence (AI) everywhere. You have heard about computers recognizing images, generating speech, natural language, and beating humans at Chess and Go. In this workshop, you will be learning the math and program the math of AI, for example we start by hand coding all the components of a neural network, without calling any librarie… more
Technical level: Intermediate
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SD
Sampriti Das How to eat an elephant?Target Corporation is a global retail organization, has around 1800 stores & a digital presence, with over 350k team members. Given the scale and scope of the organization, it is even more important to have appropriate automated management of data in place. In this paper, we would share a story about how an ETL process, primarily used for Target’s Identity and Access Management (IAM) governance w… more
Technical level: Beginner
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Sanket Sudake Machine Learning on Kubernetes using KubeflowDeploying applications with containers is now a de-facto standard & Kubernetes is a preferred orchestrator for deploying containers. Using kubernetes to build/train/deploy machine Learning application is desired considering out-of-box feature which kubernetes provides like autoscaling, self-healing, rolling upgrade support etc. more
Technical level: Intermediate
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VK
Varun Khandelwal Solving real world optimization problems using AI & MLIn this session i want to talk about how I have used ML, AI & IoT to solve real world complex business problems. Some of the problems I have worked on are: more
Technical level: Advanced
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AN
Amir Nagri Realtime System Uptime Tracking for 2M req/min@GO-JEK, we serve more than 3M+ customers daily. This involves rigorous discipline around system reliability and availability. One of the tools that helps us achieve this goal is our Realtime System Uptime Tracking. more
Technical level: Intermediate
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VN
Vidyasagar Nallapati Serverless Machine Learning PlatformA practical, architectural talk on a scalable, platform for machine learning as a service built on the principles of serverless architecture more
Technical level: Intermediate
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SUNIL KUMAR  Introduction to Deep Learning using TensorFlowThe hands-on workshop will cover basics on machine and deep learning. It will also include custom training of TensorFlow. The attendees will learn how to use deep learning tools to construct prototype of real world applications in multiple domains. more
Technical level: Intermediate
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AM
Ashish Mukherjee Crisptalk - Rule-based SMS analysis for Credit Risk InsightsThe lending space is rapidly growing in the fintech arena in India. The challenge faced by this sector is operational scaling of the business to an unprecedented level without adding large amount of manpower. In order to meet this challenge, the traditional process must be replaced in part or whole by digital data sources, one of which is SMS. The solution designed is able to analyse SMS data and… more
Technical level: Intermediate
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CK
Chandulal Kavar From Application Developer to Big Data EngineerIn this talk, I will talk about my journey from working as an App Dev to a Data Engineer at ThoughtWorks. This transition helped me to change my mindset around Big Data as well as problem-solving techniques required for distributed computing problems. I will also cover how one API over other will affect the performance and memory usage. Furthermore, I will show some of these techniques through so… more
Technical level: Beginner
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AK
Amit Kapoor Architectural Design for Interactive VisualizationVisualisation for data science requires an interactive visualisation setup which works at scale. In this talk, we will explore the key architectural design considerations for such a system and illustrate using examples the four key tradeoffs in this design space - rendering for data scale, computation for interaction speed, adaptive to data complexity and responsive to data velocity. The key take… more
Technical level: Beginner
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ravi teja When NLP meets Analytics @ Sentienz“What’s my best selling product in India in March?” more
Technical level: Intermediate
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RG
Raghunandh GS Mapping MoviesWe are a movie-loving nation. In our country, approximately 2.5 Billion movie tickets are being sold every year. We grew up watching movies and we bring up a lot of movie references in our day to day conversations. But have we ever thought of exploring and understanding movies through data? This talk will revolve around the worderful world of movies, data and design. more
Technical level: Intermediate
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AV
Anusha Venkat How does big data & data insights drive business. New trends in enterprise data servicesHow does big data & the insights drive Business for PayPal, JIO , eBay, etc. What are the new trends that provide data insights that drives business towards success. Key performance indicators and the actions surrounding them. more
Technical level: Intermediate
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AN
Abhishek Narain Enabling modern data integration in Azure cloudThis session walks through a comprehensive set of new additions to Azure Data Factory (ADF) and SQL Server Integration Services (SSIS) for moving and integrating data across on-premises and cloud. Topics and examples focus on the needs of the data integrator and data engineer for data warehousing with Big Data, business intelligence, and advanced analytics in SaaS applications. Learn how Microsof… more
Technical level: Intermediate
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SG
Satish Gopalani Generating Data Analytics Reports using Scalable Config Driven FrameworkGenerating a prolific number of Analytics Reports from 100’s of different dimensions and metrics for customers and internal stakeholders has been a critical work of BigData Analytics team at PubMatic. Writing custom jobs to provide analytic reports, leads to repetitive efforts and redundancy of business logic in many different jobs. Another challenge is scaling the platform which already processe… more
Technical level: Intermediate
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sanjiv soni Swing and a Miss: Deploying machine learning models for IoT enabled devices using PythonThe primary purpose of this talk to describe how we are using python and sklearn to model and analyse time series sensor data. In particular, I will walk through how we use Python to process data from an IoT enabled sensor attached to a cricket bat, build machine learning models on the data, and use open source tools to deploy our models in the sensor device as a smart IoT application. more
Technical level: Beginner
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AP
Aravind Putrevu Elasticsearch : Distributed System for Large Scale Data needsOften late, data explosion is a normal thing with any business. Developers collect, contain and categorize data for various use cases. more
Technical level: Beginner
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KP
Keerthi Prasad End-to-end automated data science process using Airflow.Evive is a data driven benefit navigator. We provide our 25+ million users with personalised recommendations on their health and wealth. We have 50+ models running on a daily basis for the recommendations. We receive around 500+ gigabytes of data coming from 30+ different sources, on a daily basis. more
Technical level: Beginner
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AK
Aiko Klostermann The Deep Learning Showdown: How to pick the right tool for the job?When you have a data centric problem to solve and you look for a technology to support you with this: The machine intelligence landscape can be overwhelming. I analysed the landscape using a data driven approach and condensed the outcome into a consumable from. Additionally I came to the conclusion that there is a set of questions you have to ask yourself to make the best possible choice for your… more
Section: Full talk
Technical level: Intermediate
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AA
Atl Arredondo Data Lineage Service at SlackAt Slack, the data engineering team has built tools that allow engineers and other people in the company to create their own data pipelines, run interactive queries and build dashboards. Over time the data volume, the number of datasets and the dependencies between them has increased. This has made data discovery hard and impacted the reliability and trust of our datasets. In addition, incidents … more
Session type: Full talk of 40 mins
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