Bala Subra
AWS Architect, Ebsco
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Balaji is a Boston-based developer, writer, speaker and debugger. He has expertise in infrastructure, internet technologies, and systems integration obtained through 20 plus years of working as a manager, architect and developer on projects for mission critical back end systems that have included various cloud platforms such as OpenStack, AWS and Azure. As a degreed Industrial engineer, he also has experience in distributive controls systems as a controls engineer at Honeywell. He has also worked extensively with global hedge fund industry providing SaaS solutions on the public cloud.
Beyond the Buzzwords: 42 AI Concepts Every Developer Should Know
AI is everywhere from APIs and databases to IDE plugins and code copilots. Yet even experienced developers often find AI jargon confusing or overlapping: embeddings, LLMs, tokens, transformers, diffusion models, RAG, LoRA…
This session cuts through the noise with a developer-focused walkthrough of 42 essential AI terms you should know to confidently read documentation, design AI-integrated systems, and have informed conversations about architecture and ethics.
We’ll group concepts into themes: Models, Data, Training, Inference, Evaluation, and Deployment. You’ll walk away with a solid working vocabulary, mental models for how AI fits into modern software stacks, and a quick-reference glossary to take home.
We have been “using AI” but we want to really understand how it works, this talk will give you the conceptual foundation you need.
APIs, Serverless Functions, Microservices - How to get your product quickly to market?
Do you have the next best idea? How will you quickly migrate a legacy feature to new world for almost free?
This talk will give you how to architect and implement your scenario for a cloud-oriented solution. We will share the best practices for storing your state in database; ways to decouple by events and suggested patterns for serverless. You will be equipped with taking advantage of low-cost serverless computing in a secure way and how to minimize operational costs. It will mostly focus AWS offerings like Serverless Aurora, API Gateway and Lambda functions for solutions blueprint.
AI + Relational Data: Hybrid ML + SQL Architectures
Many systems have relational / OLTP backends; how do you combine them with AI/ML inference or vector stores? --> Hybrid query architectures, caching, indexing embeddings in SQL, when to sync between vector DB and relational DB, consistency approaches
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