Snigdha Kashyap
Contributing to #Tech as SDE-2 @ExpediaGroup
Gurgaon, India
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A Software Development Engineer with over three years of experience specialising in software development (majorly backend) and cloud technology. I have contributed to robust, scalable systems across various tech stacks in previous roles at Freecharge Payments and Airtel Payments Bank. With a zeal for building efficient, user-centric applications. A problem-solver and avid technologist.
Area of Expertise
Topics
Innovating with Hybrid Agile: Merging Scrum and Kanban to Build Smarter Products
Building innovative products requires more than just sticking to standard Agile frameworks. Hybrid Agile blends the discipline of Scrum with the adaptability of Kanban, empowering product and tech teams to innovate faster and smarter.
This session will delve into
How combining Scrum and Kanban fosters a culture of continuous innovation.
Practical workflows and tools to align product vision with engineering delivery.
Real-world examples of successful product launches using Hybrid Agile.
Strategies for overcoming bottlenecks in product discovery and development cycles.
Attendees will learn how to adapt this approach for faster MVPs, seamless stakeholder collaboration, and a clear innovation roadmap.
From Chaos to Control: AI-Optimized Kubernetes Workflow Management
As cloud-native systems scale, managing Kubernetes clusters has become increasingly complex. This session introduces how AI-driven tools are transforming Kubernetes workflows, from automating CI/CD pipelines to optimizing resource allocation and anomaly detection in production. We will explore cutting-edge open-source AI projects that integrate seamlessly with Kubernetes, demonstrating real-world applications that reduce human intervention while improving efficiency and reliability.
Attendees will leave with actionable insights and practical knowledge to adopt AI-powered automation in their Kubernetes environments effectively.
Ethical decision making with Responsible AI
Introduction to responsible AI: Responsibility in the context of AI refers to ensuring that artificial intelligence systems are designed, developed, and deployed in an ethical, transparent, and accountable manner. This includes mitigating risks, protecting privacy, ensuring fairness, and making sure that AI does not harm individuals or society. Responsibility is not just about the behavior of the AI itself but also about how it is used by the people who create, regulate, and interact with it.
- Also, how different organisations define it in their ways.
In the context of AI, "Parampara" can be understood as the tradition of responsible creation and ethical use of technology. “Pratishtha" can be understood as the foundational establishment of AI systems that are built upon clear ethical guidelines and societal values. "Anushasan" refers to the discipline or regulation needed when developing and using AI systems.
Responsibilities of AI (Building AI Right). Components of the right AI
Responsibilities towards AI as developers, creators, users.
DOs and DON’Ts
Interesting references and a case study for accenture: https://www.accenture.com/us-en/case-studies/data-ai/blueprint-responsible-ai
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