The challenge with Machine Learning (ML) models is productionizing. It requires data ingestion, data preparation, model training, model deployment, and monitoring.Adopting MLOps practices is similar to DevOps practices. In MLOps, the workload changes, but some core principles like automation, continuous integration/continuous deployment (CI/CD), and monitoring. Taking DevOps practices, I will discuss the similarities and differences in adopting MLOps practices.In this talk, Chinmay takes a production use case to scale ML models to 2 million+ daily requests. It leverages Google Cloud's (GCP) infrastructure to use its GPU and other services. This talk will help you draw similarities between DevOps and MLOps as a DevOps practitioner and help you learn how to run Machine Learning models at the production scale with best practices.

Silvio and Carole introduce lstk, LocalStack's next-generation CLI built from scratch in Go. They explain why the team rebuilt the CLI, walk through its key features — zero-config startup, seamless browser-based authentication, and a rich interactive TUI that surfaces real-time progress and actionable errors — and demo how lstk gets you from install to a running emulator in seconds. They also dive into the architecture behind lstk and how it is designed to support multiple emulators, flexible runtimes, and deep integrations with CI pipelines and IDEs.

Cloud-first development kills your inner dev loop. There's a better way.TypeDB on AWS is powerful — Lambda functions querying complex relationship hierarchies, recursive schema functions resolving transitive memberships, all without application logic. But iterating on schemas and queries in the cloud is slow and expensive.Harsh Mishra introduces the TypeDB extension for LocalStack: run a fully functional TypeDB server inside your local AWS environment and connect your app exactly as you would in production. Faster iteration, zero unnecessary spend.

Are Property Graphs living up to the hype? Maybe the model itself is the problem.We made the move from relational databases to graph databases to escape "Join Pain" and model the real world more naturally — but for many engineering teams, that promise has curdled into something worse: the Spaghetti Graph.Complex queries. Ugly workarounds for multi-party relationships. Fragile schemas that shatter with every iteration and become a nightmare to maintain.The good news? The problem isn't your data.In this talk, Joshua Send breaks down why standard Labeled Property Graphs (LPGs) fall short when applied to complex domains — and introduces TypeDB, a strongly-typed database that brings together the connectivity of a graph with the integrity of a relational model.You'll come away understanding:Why LPGs struggle at scale and complexityWhat "Spaghetti Graphs" are and how teams fall into the trapHow TypeDB's type system enforces data integrity without sacrificing flexibilityWhen a strongly-typed graph database is the right tool for the jobWhether you're deep in a graph migration, evaluating database architectures, or just tired of schema chaos — this one's for you.