Scale By The Bay
Code and Data in the Age of AI
The 10th Anniversary SBTB is coming back for its best year ever!
November 13, Workshops
November 14-15, Main event
Scottish Rite Center, Oakland
About the event
Scale By the Bay is a developers' own conference. It is a community conference with the best Bay Area meetups and technologies powering the global leaders in data, operations, ML, and the art and craft of software engineering.
Our tracks are always at the confluence of three themes: programming, distributed systems, and data. This year we are also adding the Open-Source Science track, accelerating human progress in finding new materials and cures to safeguard the future of the planet.
Since 2020, SBTB has been produced by Konfy, an all-women team of conference organizers creating events with love, by developers, for developers.
Feel the atmosphere
Gwen is a co-founder and CPO of Nile. She has 20+ years of experience working with code and customers to build reliable and scalable data architectures - most recently as the head of Cloud Native Kafka engineering org at Confluent.
Gwen is a committer to Apache Kafka, author of “Kafka - the Definitive Guide” and "Hadoop Application Architectures."
You can find her speaking at tech conferences or talking data at the SaaS Developer Community.
Nile, Co-founder and CPO
Dr. Anthony J. Annunziata leads a global team bringing together foundation model and generative AI, quantum, and high performance hybrid cloud computing to deploy a revolutionary new technology platform to accelerate discovery and solution creation in science and business.
As a core part of this mission, Anthony and team are cultivating a new community of partners, developers and scientists to advance the application of this advanced technology to challenges in health, climate, energy, manufacturing, and beyond that are crucial to societal progress and prosperity.
IBM, Director and General Manager (Product, Engineering and GTM), AI and Quantum Accelerated Science
FarosAI, Machine Learning Engineer
Leah has spent the last two decades working on information representation, processing, and modeling. She started her career as a computational neuroscientist studying sensory integration, and then transitioned into data science and engineering.
Leah worked on developing AutoML for Salesforce Einstein and contributed to open sourcing some of the foundational pieces of the Einstein modeling products. She has brought her focus on making it easy to learn from expensive to generate and collect datasets to her work in everything from job search, to sales, to biotech, to engineering productivity.
Leah currently works as Machine Learning Engineer at FarosAI (an engineering intelligence platform) developing the native AI capabilities.
Building production-ready LLM-powered applications
Josh Tobin, Gantry & Full Stack Deep Learning
The way AI-powered apps are built has changed:
* Before LLMs, an idea would bottleneck on training models from scratch, and then it'd bottleneck again on scalable deployment.
* Now, a compelling MVP based on pretrained LLM models and APIs can be configured and serving users in an hour.
An entirely new ecosystem of techniques, tools, and tool vendors is forming around LLMs. Even ML veterans are scrambling to orient themselves to what is now possible and figure out the most productive techniques and tools.
In this course, we'll teach you how to build AI-powered applications from scratch, while following the best practices that will allow you to balance shipping quickly with building high-quality, production-ready applications your users trust. We'll walk you through a structured approach to AI app development loosely based on the test-driven development methodology used in traditional software engineering.
FROM THE BEST DEVELOPER MEETUPS
SBTB started as a conference of the Silicon Valley meetups.
Our members build the new world