Shashank Kapadia
Machine Learning Engineering | Building Scalable AI Solutions | NLP & Personalization | Ethical AI Advocate | Mentor | Writer
Sunnyvale, California, United States
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Shashank Kapadia is a machine learning engineering leader specializing in large-scale AI solutions that drive measurable improvements in user engagement and business outcomes. With over a decade of hands-on experience at global organizations like Walmart, Randstad, and Monster Worldwide, he has pioneered cutting-edge ML solutions—optimizing revenue, boosting engagement, and streamlining decision-making.
Shashank’s approach balances technical rigor with ethical responsibility. He champions fairness, transparency, and real-world relevance, ensuring solutions serve both the enterprise and the broader community. An active mentor and thought leader, he has spoken at global conferences, judged and mentored hackathons, authored widely-read articles on NLP, and co-authored published research—guiding teams to award-winning results.
A valedictorian graduate in Operations Research from Northeastern University, Shashank continues to push the boundaries of ML innovation. His work exemplifies a seamless fusion of cutting-edge techniques, high-level strategy, and values-driven execution—advancing technology that’s as impactful as it is responsible.
Area of Expertise
Topics
Techniques for Scaling Large Models with Model & Data Parallelism
Discover how to train and serve massive AI models efficiently by leveraging both model and data parallelism. In this session, we’ll explore how to partition large models across GPUs and distribute data for optimal throughput, diving deep into practical setup details and performance benchmarks.
We’ll also address the key tradeoffs—such as latency vs. resource usage—and show how to tailor parallelization strategies to different AI tasks, going beyond transformers into computer vision and more.
By the end, you’ll have a holistic understanding of how to design and deploy parallelized workflows that balance accuracy, speed, and infrastructure costs, enabling you to scale AI solutions effectively in real-world scenarios.
From Concept to Scale: Engineering Scalable AI Platforms for Global Applications
Learn how to design and deploy AI platforms that scale globally, balancing innovation with operational efficiency. This talk provides actionable strategies to overcome challenges in scalability, latency, and reliability for real-world AI applications
Optimizing Learning Pathways with AI: Personalization at Scale
Discover how AI-powered systems can revolutionize education by personalizing learning pathways for students at scale. This session explores advanced techniques like recommendation algorithms, multilingual NLP, and predictive analytics to create tailored experiences that enhance engagement, improve outcomes, and ensure equity across diverse student populations
Ethical and Efficient AI: Redefining Personalization with Scalable, Privacy-Aware GPU Workflows
Learn how to build ethical, privacy-first AI systems that deliver personalized experiences without compromising efficiency. This session explores GPU-optimized workflows, including mixed precision training and quantization, alongside frameworks for ethical AI to achieve scalable, responsible, and high-performance recommendation systems.
Building Trust with AI: Mastering the Balance Between Personalization and Privacy in Recommendation
In this session, discover how to design and deploy ethical AI recommendation systems that delight users while respecting their privacy. We’ll explore real-world strategies for mitigating algorithmic bias, protecting sensitive data, and complying with emerging regulations—without sacrificing the personalization that drives engagement. Attendees will learn scalable, actionable techniques that set the foundation for building user trust, fostering innovation, and staying ahead in today’s AI-driven marketplace.
Ethics in AI: Balancing Personalization and Privacy in Recommendation Systems
Explore best practices for deploying ethical AI in recommendation systems, balancing user personalization with privacy concerns, leveraging scalable applied ML solutions, and mitigating algorithmic biases.
Maximizing Performance in MLOps: GPU Optimization with Mixed Precision and Quantization
Unlock the full potential of GPU acceleration in MLOps by integrating mixed precision training and quantization techniques. This session provides a deep dive into scalable workflows that reduce computational costs, improve inference latency, and streamline distributed model deployments
Accelerating Automation with Mixed Precision and Distributed AI Pipelines
Discover how mixed precision techniques and distributed AI pipelines can revolutionize automation by enhancing the efficiency of training and inference. This session explores practical strategies for accelerating AI workflows, reducing latency, and scaling deployments for real-world automation applications.
PlatformCon 2025 Sessionize Event Upcoming
Automate 2025 Upcoming
Optimized AI Conference Sessionize Event
DevFest Fresno 2024 Christmas Edition Sessionize Event
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