talk-data.com

Talk: A live, end-to-end demo of wiring an open-source vision-language model (SmolVLM) into Vertex AI - with a lightning primer on what Vertex AI is, which quotas matter (GPUs), and how to pick the right model tier for your latency x cost sweet spot. We’ll then drive that endpoint from a Firebase web app that streams camera frames and spits back analytics in milliseconds - real-time video AI minus the heavyweight MLOps baggage. Target Audience: Cloud and DevOps engineers, full-stack developers, AI-ML hobbyists, and startup builders already shipping (or keen to ship) on GCP who want a pragmatic path to weaving generative/computer-vision AI into their products and pipelines. Takeaways: A checklist for matching model size/tier to performance and budget. A lightweight pattern for streaming video to that endpoint via Firebase and turning frames into instant insights.

Many engineering teams embrace the you build it, you own it mantra. But what happens when this leads to operational friction instead of empowerment, especially as you scale? This talk discusses Grammarly's five-year journey transforming our engineering platform, supporting 500+ engineers, from a complex necessity into a strategic asset that genuinely boosts developer productivity. I'll share the steps we took to overcome common hurdles, reducing onboarding from weeks to days and project setup from hours to minutes. We didn't tinker around the edges; we rethought our core philosophy. This involved establishing a unified developer experience, creating Golden Paths for core processes, building effective self-service systems, and ensuring technical compliance without stifling autonomy. This inside look shares not only our wins but also the pitfalls we encountered along the way. My aim is to offer you a candid look at how we made our platform work for our developers so you can apply these insights to your engineering environment. Attendee takeaways: A practical blueprint for evolving the ownership: what to automate, what to leave free. Tactics that win executive support for bold platform bets. An example of implementation for Platform as a product.