Audrey Webb
I’m a Machine Learning Engineer with experience spanning data science, data engineering, and AI platform development across research, enterprise, and product driven environments. I began my career in public health and infectious disease research, working with academic and government partners on large scale statistical modeling, record linkage, and population-level analysis. I later transitioned into industry, where I’ve built and deployed production-grade data and machine learning systems that power real world products.
My work has covered the full ML lifecycle, including data pipelines, feature stores, model training, explainability, observability, and large scale deployment. I’ve contributed to recommendation systems, ranking and propensity models, generative AI applications, and MLOps platforms that enable teams to ship and maintain models reliably in production.
Beyond my core roles, I’ve worked on applied AI and consulting projects, including an AI powered analytics platform for higher education institutions, and advisory work on computer vision and automation for e-commerce workflows. I’m especially interested in scalable and interpretable AI, responsible deployment, and making advanced AI systems practical for real world teams. Outside of work, I enjoy mentoring, volunteering, traveling, and engaging in conversations around AI education, ethics, and regulation.
Session
AI tooling is moving fast, but many Python developers are unsure where to start or how today’s AI patterns fit into systems they already know how to build. This talk is a practical, hands-on overview of modern AI development patterns in Python, focused on what you need to know to go from zero to hero.
We’ll walk through a real-world coding example broken into parts that illustrate the core building blocks of modern AI applications, and explain when each pattern makes sense. This example is designed in a way that doesn’t require any prior machine learning experience, and attendees will leave with an understanding of how AI systems work, what problems they’re good at solving, and how to maintain and observe what has been built.
Topics we’ll cover:
The modern AI stack in Python: LLM APIs, embeddings, tools, and agents
Common Python AI patterns: prompts, function calling, RAG, and simple agents
When to use a script vs an agent vs a service (and when not to)
How to get something working quickly without sacrificing reliability or safety
Practical guardrails: handling errors, controlling outputs, and protecting data
How to generally stand up common AI workflows, such as LLM-powered scripts to lightweight AI agents / MCP-style services.
Attendees will leave with a clear map of the AI landscape, working Python patterns they can reuse immediately, and the confidence to start building AI features without needing a machine learning background.