​Join us at AI Loves Data for an exclusive virtual experience focused on THE FUTURE OF APPLIED AI IN RETAIL & E-COMMERCE— a day designed to connect you with the right people, without any surprises.

​AI Loves Data Virtual is an annual one-day conference, limited to vetted attendees, focused on using artificial intelligence, data science and machine learning in reatail and e-commerce.

​Expect to walk away with actionable insights from those working on the frontlines of AI and machine learning inreatail and e-commerce.

​AI Loves Data Virtual | Event Schedule 2026

​June 17, 2026

​🩷 11:05 AM – 11:35 AM
Speaker: Ahsaas Bajaj, Sr Machine Learning Engineer at Instacart

​Session: Building Production-Ready Recommendation Systems: Lessons from Product Substitution at Scale

​This session presents a practical case study of Instacart’s product substitution system, highlighting the real-world engineering tradeoffs behind deploying recommendation models at scale. Rather than focusing on novel algorithms, it examines challenges like cold-start handling, retailer-specific constraints, personalization vs. robustness, and when simpler models outperform complex ones in production. The talk also covers monitoring, iteration, and operational evolution, offering data scientists and ML engineers concrete lessons on building and maintaining reliable, large-scale recommendation systems under real-world constraints.

​🩷 11:35am - 12:05pm
Speaker: Kelly Vincent, Data Scientist at Hill’s Pet Nutrition

​Session: LLM AI Hallucinations

​🩷 12:05pm - 12:35pm
Speaker: Palash Arora, Sr Data Scientist at Kroger Co.

​Session: Structural Sources of Bias in Applied Causal Inference

​This session examines four recurring statistical pitfalls in industry experimentation that can distort high-stakes business decisions. Drawing on a decade of experience leading A/B tests and causal impact analyses, it highlights common but often overlooked errors that introduce bias or inflate variance in contexts such as pricing, promotions, and ad optimization. Through concrete examples, the talk explains the underlying statistical mechanisms, provides clear diagnostic approaches, and offers practical remedies for designing more reliable and defensible causal analyses.

​🩷 12:35pm - 1:05pm
Speaker: Prasanth Yadla, Sr Machine Learning Engineer at Apple

​Session: Long-Term Memory Architectures for AI Agents: Scaling Knowledge Over Time and Context

​As AI agents evolve into long-running, autonomous systems, designing effective long-term memory becomes essential. This session explores practical, production-ready memory architectures, including hierarchical and vector-based storage, multi-modal embeddings, and strategies for refresh, pruning, and consistency. It also addresses real-world challenges such as staleness, retrieval latency, and context explosion, with guidance on integrating memory into planning and control modules. Attendees will gain actionable patterns for building scalable memory systems that enable agents to reason, recall, and operate reliably in dynamic environments.

​☕️ 1:05pm - 1:20pm

• ​15 Min Break

​🩷 1:20pm - 1:50pm
Speaker: Shruti Jalan, Applied Scientist at Amazon

​Session: Experiment Whisperer: How GenAI Decodes the Secret Language of Historical A/B Tests

​This session explores how large-scale A/B testing programs can evolve from isolated experiments into reusable knowledge systems. By systematically analyzing historical test repositories, organizations can identify patterns, avoid repeated pitfalls, replicate successful strategies, and improve the design of future experiments. The talk also examines how AI and autonomous agents can mine vast experimental datasets to enhance statistical precision, reduce required sample sizes, and generate insights beyond manual analysis—turning past experimentation into a compounding asset that accelerates innovation and decision-making.

​🩷 1:50pm - 2:20pm
Speaker: Koteswara Rao Chirumamilla, Lead Data Engineer at Albertsons Companies

​Session: Multi agent System(MAS) root cause analysis

​This session presents a Multi-Agent System (MAS) framework for automating KPI root-cause analysis in complex, cloud-native enterprise environments. As distributed microservices, hybrid architectures, and dynamic workloads make manual triage increasingly difficult, the proposed architecture uses domain-specialized agents to monitor signals, correlate anomalies, and reason across temporal and causal relationships. By combining distributed decision-making, shared knowledge graphs, rule-based inference, and ML-driven anomaly detection, the approach reduces MTTD and MTTR while improving explainability—offering a scalable model for proactive reliability engineering in modern data platforms.

​🩷 2:20pm - 2:50pm
Speaker: Apoorva Modali, Principal Data Scientist at Walmart, Founder Ovie's Lab Walmart Inc, Ovie's Lab

​Session: Applying Enterprise AI Thinking to Build Safety-Sensitive Consumer Products

​This session explores how enterprise AI decision-making practices can be adapted to safety-sensitive consumer products such as topical and ingestible wellness solutions. Using the Evidence-First Functional Formulation Design (EFFFD) approach, it focuses on structuring decisions around clearly defined problem boundaries, required evidence, and explicit guardrails rather than model performance alone. Through practical examples, the talk demonstrates how to evaluate uncertainty, manage trade-offs, and make defensible, evidence-bounded decisions in environments with limited data and real-world constraints—offering an actionable framework for extending AI rigor into consumer-facing contexts where trust and safety are critical.

​🩷 2:50pm - 3:20pm
Speaker: Vinodhkumar Gunasekaran, Principal - Global Innovation & Analytics at Circana

​Session: Agentic AI: The Future Of Forecasting In An Evolving Market

​This session examines how forecasting is shifting from static prediction engines to agentic, goal-driven systems that continuously adapt to changing conditions. Drawing on applied experience in the CPG industry, it explores how agent-based architectures extend strong statistical foundations to dynamically interact with pricing, supply chain, promotion, and operational constraints in volatile environments. The talk highlights how agentic forecasting differs from traditional predictive models, the importance of robustness and uncertainty management, and how multi-agent coordination can improve decision quality while preserving statistical rigor—offering a practical view of what works in production and where the real value lies.

​☕️ 3:20pm - 3:35pm

• ​15 Min Break

​🩷 3:35pm - 4:05pm

​Speaker: Tanushree Mehra, Sr Data Scientist at Airbnb

​Session: From Black Box to Glass Box: Analytics for Model Explainability & Observability

​This session presents a practical framework for model explainability and observability across the full ML lifecycle, combining local and global explanation techniques with system-level monitoring. Designed to support legal, compliance, and policy stakeholders alongside technical teams, the approach enables models to be interpreted, validated, and audited in production. It also supports continuous monitoring and drift detection at scale, significantly improving anomaly identification and reducing manual investigation effort—offering a blueprint for deploying transparent, accountable AI systems in enterprise environments.

​🩷 4:05pm - 4:35pm
Speaker: Arunkumar Amaran, Manager Data Engineering at Macys Systems

​Session: AI in Retail: From Intelligent Automation to Autonomous Decisioning

​This session explores how retail is shifting from AI-driven optimization to AI-driven decision-making in real time. Moving beyond traditional forecasting and recommendation systems, the next phase of retail technology centers on autonomous, self-learning systems that perceive, reason, and act across merchandising, supply chain, and customer experience. The talk examines how foundation models, conversational analytics, and human-in-the-loop design are enabling end-to-end AI systems that function as decision copilots—augmenting human judgment at scale rather than replacing it.

​🩷 4:35pm - 5:05pm
Speaker: Shashwat Jain, Sr. Software Development Engineer at Amazon

​Session: Defending E-Commerce Platforms from Generative AI Bots Using Real-Time Behavioral Intelligence

​This session examines how advances in generative AI and agentic browsing frameworks are enabling sophisticated automation in large-scale e-commerce environments, from inventory scraping and credential stuffing to automated purchasing and API abuse. As these systems increasingly evade traditional bot detection, the talk focuses on using behavioral intelligence and real-time signal analysis to identify synthetic user activity at scale. Drawing from production environments, it outlines scalable detection architectures that analyze session-level patterns, distinguish legitimate interactions from agent-driven activity, and mitigate emerging automation threats—helping preserve platform integrity, performance, and fairness in high-traffic digital ecosystems.

​🩷 5:05pm - 5:35pm
Speaker: Ajay Kumar Boddepalli, Staff Data Scientist at Walmart

​Session: Shipping Speed Elasticity Estimation Using Causal Inference Machine Learning Techniques

​This session explores how to determine which e-commerce items are truly “speed-sensitive,” balancing customer satisfaction with margin control. Using large-scale historical sales data, it addresses the challenge of observational bias—where promotions, seasonality, and external factors distort demand signals. The talk outlines how causal inference techniques combined with deep learning can isolate the true lift generated by faster shipping, including approaches such as Inverse Propensity Treatment Weighting (IPTW), two-stage modeling architectures, and scalable backtesting across millions of SKUs. By emulating randomized controlled trials using observational data, the framework enables data-driven shipping decisions without costly live experimentation, helping optimize revenue and operational efficiency at scale.

​Expect to walk away with tactical insights from those leading AI transformation within their organizations.

​About AI Loves Data

​AI Loves Data is head quartered in Miami and is the leading provider of unique content and a diverse, vendor-neutral community for data scientists, machine learning engineers, and other subject matter experts. Since 2016, we have been driving knowledge sharing, best practices, and innovation in data science, machine learning and AI. With our extensive digital platform of content, webinars, training, and podcast, we cater to the evolving needs of our fast-growing and diverse community. Join us to access valuable resources, engage in insightful discussions, and be part of shaping the future of data science, machine learning and AI. 

​Refund and Ticket Transfer Policy

​Refunds are available up to 30 days prior to the start of the event.
Tickets may be transferred to another attendee up to 48 hours before the event. No exceptions.