Abstract: 

The demand for faster and more efficient data science workflows has never been higher. Traditional approaches to data analysis, model development, and experimentation are often slow and resource-intensive, limiting the pace of innovation. This session will explore how AI-powered automation and GPU acceleration can 10x the speed at which data scientists develop, analyze, and extract insights.

We will start by tackling AI-driven code generation and execution using an intelligent AI editor that seamlessly integrates generative AI to assist with writing and running code for data-driven decision making. From automatic data preprocessing to exploratory analysis, we will take advantage of generative AI tools to reduce friction in the development process, allowing data scientists to focus on high-value tasks rather than repetitive scripting.

Beyond automation, we will take a technical look into the power of self-hosted custom LLMs and how they can be optimized for data-intensive workloads. Whether fine-tuning models on proprietary datasets or running inference at scale, hosting LLMs on GPU-powered infrastructure unlocks performance and efficiency with the best open-source LLM models. We’ll also cover best practices for provisioning GPU servers across multiple cloud providers, ensuring that AI workflows remain both scalable and cost-effective. General compute and cloud GPUs will be provided to attendees.

Attendees will gain practical insights into how they can supercharge their workflows by leveraging AI and GPUs to dramatically accelerate their time-to-insight. Whether you’re a data scientist, ML engineer, or AI practitioner, this hands-on workshop will provide actionable strategies to optimize your workflow and maximize computational efficiency.

Bio: 

Andrew Chang is the Founder and CEO of American Data Science, a startup building AI developer tools for data scientists, researchers, and AI engineers. He specializes in the intersection of AI and data analytics, with a background in machine learning infrastructure and software engineering. Previously, he was the AI Tech Lead at an a16z-backed Series B startup focused on building a universal knowledge graph. Now he is focusing on building out Alph, the AI notebook workspace for data scientists and researchers.