Aditi Gupta
Empowering Cloud Security: Harnessing AI for the Future
New Delhi, India
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Aditi Gupta is a Software Engineer at Disney+ Hotstar, working on high-scale distributed systems serving millions of users. Her work focuses on performance, reliability, and observability under high-concurrency conditions.
She has experience in cloud-native technologies, including containerisation, microservices architecture, and Kubernetes-based orchestration. Her interests span across distributed systems, cloud-native platforms, and AI/ML-driven workflows with current focus on applying distributed systems and observability principles to AI-driven workflows - making agentic systems more traceable, debuggable, and production-ready.
She has presented her work at international conferences, with a focus on understanding system behavior, tracing complex workflows, and debugging failures in large-scale systems.
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Poisoning the Well: Security Lessons from Vulnerability Research in Telemetry Pipelines
Observability pipelines are the nervous system of modern cloud-native infrastructure. However, the streaming platforms, vector databases, and log aggregators powering these environments have become high-value targets for threat actors. This session presents a real-world case study centered on security research that uncovered and reported critical vulnerabilities across enterprise streaming architectures and API infrastructures. The presentation explores how systemic architectural misconfigurations can expose data pipelines to severe exploits like DNS rebinding, Server-Side Request Forgery (SSRF), and malicious payload manipulation.
Moving beyond the exploits, the case study provides a detailed account of the remediation and hardening process. Attendees will learn practical strategies for integrating robust DevSecOps practices directly into their observability deployments. The speaker outlines how to enforce secure network boundaries around centralized data stores and utilize open-source tools like Prometheus and Fluentd to actively monitor the pipeline's health, ensuring the integrity of the telemetry stream against malicious exploitation.
No More GPU Cold Starts: Making Serverless ML Inference Truly Real-Time
Serverless ML inference is great but when GPUs are involved, cold starts can turn milliseconds into minutes. Whether scaling transformer models or using custom inference services, the startup latency caused by container initialization, GPU driver loading, and heavyweight model deserialization can kill real-time performance and cost you tons of money.
In this talk, we'll break down the anatomy of GPU cold starts in modern ML serving stacks including why GPUs introduce unique cold-path delays, how CRI and device plugins contribute to it, and what really happens when a PyTorch model boot-up on a fresh pod.
We’ll walk through production-ready strategies to reduce startup latency:
- Pre-warmed GPU pod pools to bypass init time
- Model snapshotting with TorchScript or ONNX to speed up deserialization
- Lazy loading techniques that delay model initialization until the first request
Thus helping you eliminate cold start pain and keep your services fast, efficient, and production-ready.
Beyond Throughput: Production Lessons from Running Write-Heavy Valkey Clusters
Synthetic benchmarks measure how fast Valkey can run. Production measures how long it remains predictable, and those are different questions.
Under sustained, high-volume write ingestion, as in a production-scale CDC pipeline, raw throughput is rarely the limiting factor. Instead, clusters become unstable when replication falls behind during resharding, memory amplification exhausts available headroom before eviction recovers capacity, or failover triggers large-scale reconnect storms.
These failure modes rarely appear in benchmark reports, but they're often responsible for production outages.
Using a production-scale CDC workload as the reference system, we'll investigate how Valkey behaves under sustained write load, exploring:
* Why is replication an early warning signal of cluster instability?
* How do resharding, replica promotion, and failover interact under write load?
* Why adding replicas doesn't always improve resilience.
* The operational signals and metrics that reveal instability before users do.
You'll leave with practical heuristics for capacity planning, shard sizing, observability, failover testing, and operating Valkey clusters under sustained write pressure.
Who Watches the Watchers? From Closed Observability to Open Control at Scale
Observability is your safety net - but at scale, it often fails first. As our Kubernetes platform grew, rising traffic produced more telemetry, overwhelming our stack and leaving us blind during incidents. High-cardinality metrics exhausted memory, and ingestion brownouts became recurring nightmares. We didn't have too much data; we had too little control.
In this talk, we dissect the concrete failure modes and show how we rebuilt observability by treating telemetry pipelines as first-class distributed systems with OpenTelemetry, Prometheus, Loki, and Tempo.
We will walk through production fixes:
- Active Traffic Shaping: OTel Collectors for batching and tail sampling.
- Defusing Cardinality Bombs: Prometheus recording rules to stabilise memory.
- Back pressure & Limits: Surviving 10x traffic spikes.
This isn't a tool comparison. It's a blueprint for building observability you can reason about under stress, so your monitoring doesn't become the “next outage” you're explaining.
Scaling ML Smarter: Optimizing Kueue & Volcano with Adaptive Scheduling
Kueue and Volcano are leading the charge in orchestrating large-scale distributed ML jobs. But are they truly maximizing your GPU resources? Traditional batch scheduling methods often suffer from inefficient queue management, and rigid allocations that fail to adapt to real-time demand resulting in problems that scale with workloads.
This talk dives into how priority-aware queueing and elastic resource allocation can supercharge Kueue and Volcano, making batch scheduling more adaptive and efficient. We’ll break down the scheduler’s architecture, exploring how jobs dynamically move between priority queues, how elastic scheduling adjusts resource allocations in real time, and how these improvements lead to faster job execution and better GPU utilization.
Whether you're managing distributed training, hyperparameter tuning, or large-scale inference pipelines, this talk will provide the tools and strategies needed to unlock smarter scheduling and maximize ROI on Kubernetes GPU workloads.
Reinforcing the Future of GPU Scheduling in Kubernetes
Scaling up Multi GPU setup using Kubernetes for large-scale ML projects has been a prominent topic in both AI and cloud community. While Kubernetes already have the capability to provide computing power by scheduling GPU nodes, certain issues like resource fragmentation and low utilization plague the performance and result in cost issues.
Why Reinforcement Learning (RL) in particular one would ask. Well, unlike the other solutions, RL shines in its unique ability to continuously adapt to changing environments and efficiently handle Complex and Multi-dimensional Objectives making it particularly suitable for the dynamic and heterogeneous nature of Kubernetes clusters.
In this talk we shall explore the current landscape of GPU scheduling and some state-of-the-art RL algorithms proposed for scheduling. Their current impact on Kubernetes and the possible use of RLHF shall be dived deep into, hoping that the audience could gain more insights into the future of scheduling GPUs on Kubernetes.
Bridge Infrastructure & Application - OpenFeature for Dynamic Feature Management in K8 Deployments
As cloud-native systems scale, managing application features dynamically without disrupting services is a cornerstone of modern software delivery. In this talk, we'll delve into the integration of Kubernetes with OpenFeature, a powerful open standard for feature flag management, to bridge application logic with infrastructure orchestration.
The session will explore Kubernetes-native resources like ConfigMaps, Secrets, and external flag services to store and manage feature configs, enabling real-time toggling without service restarts. Combined with Kubernetes' rolling updates, it ensures rapid recovery from critical issues, safeguarding system stability. This integration redefines how developers roll out features, offering unprecedented control for A/B testing, and canary deployments.
By combining K8s' orchestration power and OpenFeature’s runtime control, this approach not only redefines feature management but also aligns with the future of scalable, adaptive cloud-native ecosystems.
Observability Summit Europe 2026 Sessionize Event Upcoming
ValkeyConf 2026 Sessionize Event Upcoming
KubeCon + CloudNativeCon India 2026 Sessionize Event
Open Source Summit India 2026 Sessionize Event
KubeCon + CloudNativeCon Europe 2026 Sessionize Event
KubeCon + CloudNativeCon North America 2025 Sessionize Event
AI_dev: Open Source GenAI & ML Summit Europe 2025 Sessionize Event
KubeCon + CloudNativeCon India 2025 Sessionize Event
CNCF-hosted Co-located Events Europe 2025 Sessionize Event
CNCG Chandigarh Meetup Sessionize Event
KubeCon + CloudNativeCon North America 2024 Sessionize Event
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