Accepting submissions till 04 Jul 2023, 12:30 PM
Not accepting submissions
Submission guidelines and selection criteria
If you are interested in speaking at The Fifth Elephant, note the following guidelines:
- Submit a description of your talk, explaining the problem that your talk covers, and one concrete takeaway for audience. Talks have to give at least one practical insight to the audience.
- Preference will be given to talk experiential talks such as case studies, journeyman stories and implementation stories.
- Editors will comment on the description. Make sure to watch out for comments from Nischal HP and Sumod Mohan on your submission. Editors’ decision on which talks are selected for the conference will be final. Talks which are not selected will also receive feedback, so that speakers can present their talks at other opportunities under The Fifth Elephant umbrella.
- If you submission is included in the shortlist, you will be required to prepare an outline of your talk and go through a rehearsal.
The call for submissions will be closed on 30 June. In the meanwhile, talks will be selected on a rolling basis, as individuals make submissions.
Speaker honorarium
The Fifth Elephant membership funds pay for honorarium for speaker. The honorarium amount will be declared with the funds available in the membership corpus on 31 July 2023.
Travel for outstation speakers
The Fifth Elephant conferences are financed by memberships. Travel grants are available subject to funds available in the corpus.
Topics for submission
You can submit an experiential talk on one of the following topics. You are welcome to submit talks on other topics. Focus on the relevance of the talk, who are the stakeholders who will be impacted by your talk, and what is the takeaway for them.
- AI and research.
- Decisions Intelligence Systems.
- Responsible AI.
- Privacy.
- Industrial applications of ML - implementation of AI, with more focus on the AI models, the issues in training, gathering data so, and so forth in industries such as automotive, mechanical, manufacturing, agriculture, and such. Explain the challenge you are working on, and the innovation coming out of these industries as they pursue ML on a second-to-second basis.
- Machine Learning (ML) application lifecycle at an organization and how it is helping the organization scale.
- Cloud or tools associated with ML application lifecycle.
- Data labeling and classification.
- Data security, data privacy and data governance.
- “Build versus buy” experiential case studies.
The conference is also accepting:
- Lightning talks on ideas and innovations that individuals are testing/implementing using AI.
- Showcase for open source projects built for MLOps.
If you have questions about submitting a talk or speaking at the conference, post a comment here.
Make a submission
Accepting submissions till 04 Jul 2023, 12:30 PM
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AJ
Arjun Jain Bumpy Roads, High Speeds: My Unexpected Journey from PhD to Tech EntrepreneurshipBackground In 2015, I sold the intellectual property (IP) of my Silicon Valley company, Perceptive Code LLC, to Mercedes Benz. Subsequently, I was tasked with meeting certain milestones as part of the handover process. I chose to complete this task in India, where I aways wanted to be. I successfully downsized our research model, initially consuming 6GB of GPU memory, to a mere 300KB of weights. … more
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AD
Abhinav Dadhich Learnings from Building Deep Learning Models for Better Cardiac CareAbstract: Echocardiogram(Echo) is one of the common modality that captures state of the heart in the form images and videos. Using Ultrasound technique, an echo study captures multiple cross sections of the heart, termed as Views. Cardiologist utilizes measurements on the basis of these Views to analyse heart functions. In order to automate this process to measure how heart is functioning, an ini… more
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Saurabh Karn Jugalbandi.ai: Powering breakthrough Conversation AI for every IndianIntroduction The gap in access to Justice in India is a huge opportunity for AI. Initial field trials from Jugalbandi with farmers, domestic workers and waste pickers presents several lessons in AI product design for all Indians. In this talk we will gain a deeper understanding of the opportunities in addressing access to justice, the design choices in building Jugalbandi and what kind of technic… more
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Priyanka Banik Adaptive Metric Alignment for Demand Forecasting in Swiggy InstamartLink to presentation: https://docs.google.com/presentation/d/1ZaA3TdTqBHurUJV7ngEhxVvZOkTM5D0vMI21qdI48kI/edit#slide=id.p1 Problem statement: Instamart, the quick commerce grocery delivery service of Swiggy gives unparalleled convenience of being able to order, from a huge assortment, across fresh fruits & vegetables/ dairy/FMCG products and accessories for household requirements, parties or fest… more
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Schaun Wheeler What do you do when A/B tests aren't enough? Validation of massively-parallel adaptive testing using dynamic control matchingA/B testing is a widely-used paradigm within marketing optimization because it promises identification of causal effects, because it is implemented out of the box in most messaging delivery software platforms, but mostly because it is held up as a “gold standard” for evaluating options. This talk will explain why A/B tests are not a particularly sound method, why businesses rarely choose better (… more
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Narayanan Subramaniam  Sustainability: Design Considerations for Real-World ML-based Process Model Training & IT/OT-to-SaaS Data Integration for Industrial DecarbonizationLink to presentation: https://docs.google.com/presentation/d/18IgJG7hvPgcQZOVoE9cQpYlp0B6fLrwd/edit?usp=sharing&ouid=116463250234490514040&rtpof=true&sd=true more
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Time-series modelling for demand forecastingLink to presentation: https://drive.google.com/file/d/1hH90FCWxRFv0IQcoBqX2yRM1DcTzDBQr/view?usp=sharing more
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Meghana Negi Solving for explainability of fraud detection modelsProblem : At the TnS(Trust and Safety) team at Swiggy, building powerful fraud detection models that operate at high precision while still capturing maximum fraud has been the uber goal. Our system currently operates at a high level of complexity through various interventions, modelling techniques, and semi-supervised training methods while maintaining robustness. For the final downstream model, … more
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Ajeevansh Gautam Elevating High-Quality Calls: Harnessing the Synergy of ML and Innovative Architecture at SquadStackProblem Statement In our journey at Squadstack, we encountered a significant challenge in maintaining exceptional customer interactions during telemarketing calls. We realized the need to evaluate callers based on various parameters to identify and flag undesirable interactions, this would become a robust solution and will help streamline the evaluation process and provide targeted training inter… more
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Arvind Saraf Title: Building real-time video-based analytics productAbstract: Companies are now building products using real-time computer vision and machine learning of video from various systems and processes. This requires a full stack system consisting of video ingestion and storage, live inferencing, post-processing of data generated, and use of the data in the business context or customer’s domain language. We demonstrate a preferred architecture and stack … more
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Rohit Agarwal 7 mistakes while deploying GenAI apps in productionI’ve built production LLM systems and made multiple mistakes. The talk will focus on key areas to focus on while deploying LLM and Generative AI systems in production more
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Sandeep Joshi Unraveling the Identity Puzzle: Disambiguating Users across Social Media and Email Platforms<H2> Link to presentation : </H2> https://docs.google.com/presentation/d/18OpwCY9uTD2v6heJjqxCqioHLGCBR7P2T71XKVKpqmQ/edit?usp=sharing more
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Ved Vasu Sharma A Customised Speech Recognition System for the Indian Telesales IndustryAbout the Industry Telesales services involving sales of products or services over a phone call is a vast market with a global value of approximately US$ 27 billion in 2022 and is expected to grow to US$ 55 Bn by 2029. Call centre solutions originating from India are a widespread phenomenon and most of these services are for the vast consumer market in India with calls happening in Indic language… more
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Meghana Negi Transforming COD from a Risk to Growth lever using Machine LearningProblem Paying for deliveries using cash after the delivery is made is a popular mode of payment employed by customers transacting online for the first time or those that prefer to have more control, especially in emerging economies like India. While the cash (or pay)-on-delivery (COD or POD) option helps e-commerce platforms, for example in our food delivery platform, tap into new customers, it … more
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Sai Representation and Reasoning on dynamically composed multimodal structuresAgenda: 1, A deep understanding of Multimodality as a phenomenon 2. The gist of the Axiomatic treatment of multimodality - covering challenges pertaining to Representation, Alignment, Reasoning, Generation, Transference and Quantification 3. The heart of Graph Neural Network Modeling frameworks including GCNs, Graph Attention and their capacity to model Heterogeneity and spatio-temporality 4. How… more
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Vaishnavi A B Driving profitable growth with data and predictive modeling in startups.By using advanced analytics and predictive modeling, growth systems have real-time access to customer journey insights. With this data and real time segmentation and prediction models, each stage of the marketing funnel can be optimized to more effectively nurture leads to convert. Which inturn helps drive up ROI. more
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Piyush Makhija How "Not" to Add a Generative AI layer to Your ProductAbstract Artificial Intelligence, particularly generative models, are driving unprecedented transformations in products and services across industries. However, integrating generative AI is not just a matter of “plugging in” the technology. more
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Shuaib Ahmed S Synthetic Sorcery: Fooling Neural Networks with Unreal Data for Real-World ApplicationsThe integration of computer vision technologies in the automotive industry has revolutionized various aspects of vehicle safety, navigation, and driver assistance systems. However, developing robust and accurate computer vision models for real-world scenarios necessitates large-scale, diverse, and accurately labeled datasets, which can be challenging to obtain. Consider a scenario where the durat… more
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Vibhav Agarwal Edge-Based Recommendation Systems: Empowering Personalized Experiences at ScaleAbstract/Proposal Server-driven recommendation systems (RecSys) face significant challenges when it comes to scaling to handle large data volumes and providing real-time recommendations. At Glance, we serve personalized recommendations to over millions of users, prioritizing response time and data privacy. To tackle these challenges, we have turned to edge machine learning (ML). By deploying ML m… more
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Harshad Saykhedkar Demystifying Quantisation in Large Language Models in Plain English with Basic MathDemystifying Quantisation in Large Language Models in Plain English with Basic Math more
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Navin Pai Bespoke LLM Architectures for a K8s WorldAbstract With the advent of Large Language Models (LLMs) and Generative AI (GAI), there is growing interest in building LLM-driven applications.. Within the industry, a growing number of companies are exploring whether they can leverage state-of-the-art, open source-driven LLM Models within their own existing k8s clusters and building applications that leverage these bespoke, in-cluster LLMs for … more
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SHRINATH BHAT Reliability Analysis of Machine Tools Using Machine LearningABSTRACT Submitted by Shrinath Bhat Mechanical Engineering Dept., IIT Madras 2020 graduate Senior Data Scientist, BEES Algo Selling team, AB InBev more
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Shiv Bhosale Unleashing Innovation: Streamlining ML Experimentation with AlchemistAbstract Glance inspires consumers to make the most of every moment by surfacing relevant experiences for them with its ‘smart lock screen’ innovation. More than 225 million consumers enjoy Glance on their Android smartphones across markets. Glance harnesses the power of Machine Learning (ML) to provide consumers with a highly personalized and engaging user experience featuring top content from b… more
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Akshat Gupta Harmonising Art and AI: Crafting Jazzy and Juicy Video Snippets through AIAbstract In recent times, Live Streaming platforms are gaining popularity where live content is being shown to users. Typically, the videos created by the creators range from 15 minutes to an hour. After intensive research, it was found that a sizable chunk of users drops within first 30 seconds of the video. Another piece of research shows that, on average, a user only has an attention span of 3… more
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Nishant Singh Efficient AI pipeline for Entity Extraction from Government RecordsAbstract: Nowadays in this digital world efficient extraction of Entities from various Government records like Pan card, Adhar card, Driving License and etc. has become a priority for various use cases like Authentication, KYC Compliance, Partner/Customer Onboarding, Age Validation etc. in a wide number of sectors. Solving such an essential problem also comes with a variety of challenges like var… more
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The Journey of Machine Learning Platform at MyntraProblem: Myntra is one of the leading fashion e-commerce companies in India. Myntra is focused on delivering best-in-class customer experience for all the fashion lovers from browsing to purchase and post purchase experience. Myntra provides curated, customized shopping experience to every user by matching deep understanding of the user with deep expertise on fashion and trends. Myntra is leverag… more
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Near Real time feature engineering at scale for machine learning use cases at MyntraProblem Myntra is one of the leading fashion e-commerce companies in India. Myntra delivers best-in-class shopping experience by leveraging many advanced machine learning models, deployed for online or real-time inference. The online inference requires streams of data to be processed, machine learning features to be computed, stored and served in (near) real-time, at Myntra scale. more
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Mradul Navigating the Credit Seas: A Unified Framework for Credit Risk Modeling in CPG IndustryProblem: The Consumer Packaged Goods (CPG) industry faces unique credit risk challenges such as fluctuating consumer demands, the risk of bad debt, optimal working capital management, and market volatility. These challenges necessitate a robust and dynamic credit risk model to accurately assess and manage credit risks. While similar problems have been addressed in the banking sector, the CPG indu… more
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Namratha Bhat Space Models: Optimizing Store Space Allocation at TargetProblem Statement In the retail industry, Category Managers and Buyers often rely on their experience and instincts when planning the allocation of space in a store. However, these traditional approaches may not be accurate or adaptable to changing market dynamics. Furthermore, numerous factors influencing space planning decisions may go unnoticed by decision makers. more
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HG
Hareesh Kumar Gajulapalli Online ML model performance benchmarking at Linkedin Scale : Implementation & ApplicationsAbstract At LinkedIn, we serve 100000s of inferences per second across 100s of ML models concurrently in our online systems. ML models have different system performance characteristics - ranging from lightweight XGBoosts to memory intensive recommendation models, to the newer Generative AI models, which are both compute and memory intensive. We run these models across different hardware profiles … more
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Transforming Document Curation: LMs and vector databases at scaleAbstract Auquan is an AI startup that serves institutional investors and investment managers with curated news and documents to help them make better investment decisions. more
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Aditi Tuli Algorithm Friendly Design: Aligning product design with algorithmic requirements.In the ever-changing landscape of digital products, algorithms play a vital role in providing personalized experiences for users. This talk will delve into the intersection of product and algorithmic design, addressing the need to create products that optimize algorithmic capabilities. For example, at Glance, a lock screen experience, we have 200m+ daily active users and need to design our user e… more
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Tuning a base language model for multi-taskingAbstract Auquan is an AI startup that serves institutional investors and investment managers with curated news and documents to help them make better investment decisions. more
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James Evolution of data science roles in the age of low-code tools and LLMsBrief : Beyond the analyst,scientist and ML engineer roles, what are the possible evolutions of roles surrounding data science and how we are seeing it develop across different projects in our agritech startup . more
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swaroopch Speaker Elevating Model Training at DoorDash with RayAt DoorDash, machine learning is a key component, used to enhance the experience of merchants, dashers, and customers. As our machine learning use cases keep growing, our forecasting and training pipelines are faced with several challenges like scalability, growing costs, reduced user development velocity and lack of proper debugging/observability. more
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Sayak Chowdhury Truck Load Optimization @TargetProblem: The modern purchasing algorithm at Target is an automated algorithm that places orders to vendors with the details of the Target warehouses (distribution centres – DCs) where these items need to be shipped. Since the DCs serve multiple stores and vendors ship multiple items across categories, a purchase order can be so big that it doesn’t fit in a single truck or so small that it doesn’t… more
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Kritika Saraswat Mitigation of Racist & Biased AI by navigating towards ethical path of innovationMy Background I am Kritika Saraswat, a passionate data scientist currently employed at AB InBev, the world’s largest brewing company headquartered in Leuven, Belgium. They own more than 500+ beer brands across the globe. With a strong background in data science and machine learning, I have been actively involved in driving impactful solutions within the domain. Prior to my current role, I gained … more
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Sachin Reinforcement Learning: From Games to chatGPT and BeyondReinforcement Learning (RL) is a subfield of machine learning that involves interacting with the environment to improve performance. Scientists have been using various games as a way to test and compare different learning and planning methods. Back in 1992, Gerry Tessauro used Reinforcement Learning to train a neural network to play Backgammon. Since then, similar techniques have been used to cre… more
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ravi theja Exploring the Power of LlamaIndex: Unlocking the Potential of LLM'sAbstract: LlamaIndex is a powerful framework that acts as a central interface between Language Model Libraries (LLMs) and external data. In this talk, we will dive into the reasons behind the development of LlamaIndex and explore its various basic building blocks/ components. We will discuss the different abstractions within llamaindex - indexes, retrievers, response synthesis, query engines. We … more
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Priyansh Saxena Vigil: Effective end-to-end monitoring for large-scale recommender systems at GlanceAbstract The success of large-scale recommender systems hinges upon their ability to deliver accurate and timely recommendations to a diverse user base. At Glance, we offer snackable personalized content to the lock screens of 200M smartphones. In this context, continuous monitoring is paramount as it safeguards data integrity, detects drifts, addresses evolving user preferences, optimizes system… more
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Somenath Sit AlgoVault - Region and platform-Agnostic Machine Learning FrameworkAbout me I am working in ABInBev as Senior Manager – Data Science with experience in Statistical/Machine Learning and Predictive Modelling and analytics consulting. With a passion for machine learning and data-driven solutions, I have been actively involved in the development and implementation of advanced analytics frameworks. Currently working as Product Owner and DS lead for AlgoVault. I am ma… more
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PR
Pradeep Rathore An Asset Management Perspective of LLM for question answering- major challenges and opportunitiesAbstract LLMs have been demonstrated to perform quite well in question answering tasks and have been shown to generate good answers based on the context provided. In many scenarios, training of LLM becomes challenging due to time and resource constraints. When it comes to adoption of LLM in large organizations, major problem arises due to the confidentially of data and scattered relevant informat… more
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Ritesh Pallod Prototype to Production in a day: the new era of AIThe last 12 months have seen a relentless pace of innovation in the field of Artificial Intelligence (AI). Each week hundreds of new models and code repositories are released. It’s clearly a wonderful time to be working in the field but the volume and pace do present their own unique challenges. How to develop products that support rapid prototyping whilst frequently changing the models powering … more
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Rahul Building Your Own Brain: The future of Enterprise Gen-AIMy name is Rahul, and I am the founder and CEO of Akaike.ai, an AI multi-modal company specialising in advanced ML/AI solutions. I am writing to you today to express my interest in speaking at The Fifth Elephant 2023 Conference on the theme of “Industrial Applications of ML.” more
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Somenath Sit Unlocking the Usage of ChatGPT: Simplifying with PyAIKitAbout me I am working in ABInBev as Senior Manager – Data Science with experience in Statistical/Machine Learning and Predictive Modelling and analytics consulting. With a passion for machine learning and data-driven solutions, I have been actively involved in the development and implementation of advanced analytics frameworks. I am master’s in computer science and have 12+ years of experience in… more
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Siddhant Agarwal Graphs are everywhereGraph databases and social graph Graph databases are the most scalable, high-performance way to query and store highly interconnected data. They help improve intelligence, predictive analytics, social network analysis, and decision and process management – which all involve highly connected data with lots of relationships. A relevant use case for graph databases is the social graph. more
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Vikas S Shetty Ensemble Techniques for Object Detection ModelsDNN (Deep Neural Network) models are nonlinear and have a high variance, which can be frustrating when preparing a final model for making predictions. In order to get good results with any model, there are certain criteria (data, hyperparameters) that need to be fulfilled. But in the real-world scenario, you might either end up with bad training data or might have a hard time figuring out appropr… more
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AJ
Ayyanar Jeyakrishnan MLOps for Enterprise using AWS SagemakerMy Previous Session MLOps for Enterprise on Sagemaker https://github.com/aws-data-usergroup-bangalore/sagemaker-mlops more
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Dhruv Nigam Predicting customer lifetime value in a non-contractual digital commerce settingAt Dream 11, we have built a Customer lifetime value(CLTV) model to predict each user’s future lifetime value. There are two broad areas where having a future-looking estimate of customer value can help. more
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Sonu Sharma  Deep Learning for Search RankingThe talk will be an implementation story which falls under #5 in the Topic for Submission - [https://hasgeek.com/fifthelephant/2023/sub#h:topics-for-submission] section. It will be mainly focused on a problem statement in the Search domain to rank the search results retrieved from the Search engine. It explains in detail about the advanced/SOTA solutions using Deep Learning, Reinforcement Learnin… more
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Vaishnavi A B Detecting and Preventing Fraud in Lending: From Rule-Based Solutions to Advanced ML ModelsOftentimes, for any new lending product the biggest challenge when trying to minimize default rates on their portfolio is fraud. more
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Simrat Hanspal Building efficient and secure vector data workflowsBackground Large Language Models have demonstrated amazing capability for solving complex problems. But they can’t answer what they haven’t seen, and to take advantage of these amazing models, we need to expose our data to the model. Fine-tuning is not an option, at least not a cheap one. Prompt engineering is a helpful technique to provide context to LLMs, which helps the model restrict its answ… more
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Srinivasa Rao Aravilli Privacy Preserving Machine Learning at ScaleIn this talk , I will talk about privacy risks with Machine Learning and explain in detail about Privacy Preserving Machine Learning techqniues. Introudce variious frameworks which can be used to implement to protect ML Models, Training Data, Inference Results from privacy threats. Talk about privacy threats in Large Lanauge Models ( LLM’s) and varous benchmarks in ML with resepct to privacy pres… more
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AD
Aaradhya Dave Analytics in Pricing for CPG IndustryAbout me I am currently a Data Science Manager in Revenue Growth Management Analytics at AB InBev, the world’s largest beer company. In this role, I harness the power of machine learning (ML) to drive business success. With my expertise in pricing and mix management, combined with a master’s degree in economics specializing in applied quantitative finance, I bring a unique blend of knowledge and … more
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Mohamed Imran K R The data bottleneck in distributed AI/ML workloadsIts a no brainer that huge amounts of data in high TBs/PBs are going to be processed for any foundational models or even training of LLMs. In this talk, i propose to discuss the pain points of handling data at this scale more
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VV
Vikram Vij Forecasting @ Samsung AdsSamsung Ads is an intuitive audience platform that delivers meaningful experiences reaching the right audience across screens, formats and devices. With more than 900M Mobiles and 150M Smart TVs, and the largest first party data set powered by ACR, we help marketers reach targets and enhance experiences that span digital landscapes. The business has grown 10x since 2015. Our foundation is based o… more
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