Akshay Mittal
Staff Software Engineer | PhD Researcher in Cloud-Native AI/ML | Passionate About Scalable & Intelligent Solutions
Austin, Texas, United States
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Akshay Mittal is an accomplished IT professional with ten years of experience as a full-stack developer and a strong interest in leadership. Currently, Akshay works at PayPal, focusing on building scalable, innovative solutions within a high-performing technical environment. He is also pursuing a part-time PhD at the University of the Cumberlands, researching cloud-native technologies utilizing AI/ML methods.
Having extensive experience as a consultant across diverse teams, Akshay quickly adapts to emerging technologies and is skilled in mastering new challenges. He holds certifications in AWS and GCP and actively mentors aspiring technologists. Akshay regularly contributes to the tech community through speaking engagements on cloud-native development and integrating AI/ML in modern software solutions. His passion lies in fostering community growth, technology leadership, and continuous learning.
Area of Expertise
Topics
ML on K8s: Running AI Workloads with KServe and Kubeflow Lite
Machine learning is increasingly moving from notebooks to production—and Kubernetes is where the action is. However, deploying scale-based models with observability, versioning, and autoscaling can get complex fast.
In this session, we’ll explore how KServe, a CNCF incubating project, simplifies the process of serving ML models on Kubernetes. Using a minimal Kubeflow-lite setup, we’ll walk through a live deployment of an ML model (sci-kit-learn or HuggingFace) and demonstrate production-grade features like autoscaling, traffic splitting, and real-time monitoring.
This talk is aimed at developers, ML engineers, and platform teams looking to operationalize AI workloads without reinventing infrastructure.
What We’ll Cover:
- What is KServe? Where does it fit in the ML + K8s stack?
- How to deploy a lightweight ML model using YAML or CLI
- Autoscaling with KNative integration
- Multi-version model rollout and routing
- Metrics, logs, and basic auth options
- Live traffic simulation to trigger scale-up
Key Takeaways:
1. Understand how KServe simplifies ML inference in Kubernetes
2. Learn how to deploy, scale, and monitor ML endpoints using CNCF tools
3. Gain insight into real-world production patterns for ML models serving
4. Leave with a GitHub repo you can fork to deploy your own models
5. Discover how to bring AI to your K8s cluster in a mini session
Infrastructure as Code (IAC) workshop
The idea behind infrastructure as code (IAC) is that you write and execute code to define, deploy, and update your infrastructure. This represents an important shift in mindset where you treat all aspects of operations as software—even those aspects that represent hardware (e.g., setting up physical servers).
DeveloperWeek Austin 2019 Sessionize Event
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