Speaker

Sonika Arora

Sonika Arora

Lead Member of Technical Staff @ Salesforce

San Francisco, California, United States

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Sonika Arora is a seasoned software engineer with over a decade of experience building scalable, resilient, and intelligent distributed systems. She currently serves as a Lead Member of Technical Staff at Salesforce, where she architects and delivers complex microservice-based platforms that power machine learning workflows at scale. At Salesforce, Sonika has played a pivotal role in designing orchestration platforms that seamlessly integrate compute services such as training, prediction, and modeling of ML jobs. By leveraging technologies like AWS Lambda, DynamoDB Streams, Kubernetes, and Terraform, she has led initiatives that ensure concurrency, reliability, and observability across distributed architectures.

Prior to Salesforce, she made significant contributions at PayPal, where she helped build real-time monitoring systems and QR code payment infrastructure—delivering solutions optimized for scale, fault tolerance, and performance.
Sonika’s strength lies in fusing backend engineering with system-level thinking to create cloud-native systems enriched with automation, monitoring, and intelligent orchestration. She remains passionate about advancing AI-powered platforms, stream processing, and high-throughput infrastructure.

Area of Expertise

  • Information & Communications Technology

Topics

  • AI
  • Machine Leaning
  • Machine Learning and Artificial Intelligence
  • Big Data Machine Learning AI and Analytics
  • Artificial Intelligence and Machine Learning for Cybersecurity
  • Artificial Intelligence and machine learning
  • women in machine learning and data science
  • BigData and Machine Learning
  • Applied Machine Learning
  • DevOps
  • Software Deveopment
  • Cloud & DevOps
  • DevOps Journey
  • Migrating to devops
  • Cloud Native
  • Kubernetes
  • service mesh
  • Cloud & Infrastructure
  • Cloud Computing

From Stream to Prediction: Kubernetes-Native Architecture for Real-Time AI at Scale

This session explores architectural principles for building real-time AI systems on Kubernetes that process streaming data and deliver predictions at scale. We'll examine how to unify streaming infrastructure with ML pipelines while addressing challenges of latency, reliability, and resource efficiency.
Key topics include:

Kubernetes CRDs for declarative streaming management
ML pipeline orchestration patterns with real-time data flows
Resilience strategies: circuit breakers, fallback models, graceful degradation
GPU scheduling and cost optimization models
Multi-tenancy and isolation designs

We'll analyze architectural trade-offs using distributed systems principles like backpressure and flow control. The session provides decision frameworks for technology selection and capacity planning, focusing on timeless patterns rather than specific tools.

Real-Time AI on Kubernetes: Streaming to Inference Architecture

This session explores architectural principles for building real-time AI systems on Kubernetes that process streaming data and deliver predictions at scale. We'll examine how to unify streaming infrastructure with ML pipelines while addressing challenges of latency, reliability, and resource efficiency.
Key topics include:

Kubernetes CRDs for declarative streaming management
ML pipeline orchestration patterns with real-time data flows
Resilience strategies: circuit breakers, fallback models, graceful degradation
GPU scheduling and cost optimization models
Multi-tenancy and isolation designs

We'll analyze architectural trade-offs using distributed systems principles like backpressure and flow control. The session provides decision frameworks for technology selection and capacity planning, focusing on timeless patterns rather than specific tools.

From Stream to Prediction: Kubernetes-Native Architecture for Real-Time AI at Scale

This session explores architectural principles for building real-time AI systems on Kubernetes that process streaming data and deliver predictions at scale. We'll examine how to unify streaming infrastructure with ML pipelines while addressing challenges of latency, reliability, and resource efficiency.
Key topics include:

Kubernetes CRDs for declarative streaming management
ML pipeline orchestration patterns with real-time data flows
Resilience strategies: circuit breakers, fallback models, graceful degradation
GPU scheduling and cost optimization models
Multi-tenancy and isolation designs

We'll analyze architectural trade-offs using distributed systems principles like backpressure and flow control. The session provides decision frameworks for technology selection and capacity planning, focusing on timeless patterns rather than specific tools.

Sonika Arora

Lead Member of Technical Staff @ Salesforce

San Francisco, California, United States

Actions

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