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I make AI products successful by making them adoptable. Over 19 years across enterprise infrastructure, data platforms, and AI, I've built a career around the same core question product managers ask every day: why aren't users getting value from this, and what do we ship to fix it? At Amazon AGI, I lead cross-functional product adoption for next-generation AI, owning the strategy, instrumentation, and execution that determines whether builders succeed or churn. I define adoption funnels, run the data to find where users drop off, and drive the product changes that fix it. I operate with full roadmap influence, partnering with Engineering, Research, and PM to prioritize what ships next based on real user behavior, not assumptions. At AWS, I shaped the generative AI go-to-market strategy from day one: identifying an unmet need for practical AI education and shipping two products that defined the category: an O'Reilly book and a DeepLearning.AI course that reached 500K+ learners. These product-market fit experiments validated demand, drove platform adoption, and strengthened AWS's market segment position. What defines my work: I don't wait for product teams to tell me what matters. I find the friction myself, through user research, funnel data, community signals, and direct builder engagement. I drive the fix cross-functionally, whether that means redesigning an onboarding flow, restructuring an API surface, or reprioritizing a sprint. I think like a PM, execute like a builder, and measure like a growth team. I build with AI every day to help shape where software engineering is heading and keep my product instincts sharp.
Own the product adoption strategy for Amazon's next-generation AI, from market positioning through user activation and retention. Leading a cross-functional effort spanning Research, Product, Engineering, and GTM to ensure what Amazon builds is what builders actually need. PRODUCT STRATEGY & ROADMAP INFLUENCE • Define and own the product adoption roadmap: Identifying the highest-leverage investments to move users from awareness to production deployment. Present adoption priorities in product reviews and influence sprint-level engineering decisions. • Serve as primary voice of the builder in product planning, synthesizing quantitative funnel data and qualitative user research into prioritized roadmap recommendations that directly shape what ships. METRICS & GROWTH • Built the product adoption measurement framework from scratch: instrumented the full funnel (discovery → activation → implementation → retention) to quantify where users drop off and why, replacing intuition-driven decisions with data-driven prioritization. • Drive go-to-market strategy for builder audiences: Influencer partnerships, conference strategy, and ecosystem programs that grew top-of-funnel awareness and measurably improved activation rates. PRODUCT EXECUTION • Define the product experience for new AI capabilities end-to-end: SDK patterns, API design, onboarding flows, and reference architectures. Ship improvements that reduce time-to-first-value, the single metric that most predicts long-term retention. • Lead cross-functional execution across Research, Product, Engineering, Marketing, and GTM, aligning five organizations around unified adoption outcomes with shared KPIs.
Promoted to the AWS DevEx leadership team. Owned the builder audience definition for the organization, the foundational scoping decision that determined where the team invested, and drove cross-functional alignment on AI/ML product strategy across Product, Marketing, and Engineering leadership. PRODUCT IMPACT • Operated as a strategic quality gate between product teams and builders: Escalated critical product experience issues to senior leadership, successfully advocating to delay a major launch to fix the builder experience before it shipped. Drove SDK improvements and embedded builder signal into product strategy documents before launch decisions were locked. • Shaped the industry narrative on agentic AI through 10+ press engagements and keynotes: Fortune, The New Stack, O'Reilly AI Superstream (chair + keynote), AI Engineer World's Fair (keynote), Open Source Summit NA, and MCP Dev Summit. Developed the messaging framework for AWS's agent interoperability story that PR, product marketing, and field teams carried through the year.
Led the generative AI product adoption strategy for AWS during the 2022–2024 market formation period, the critical window when platform choices were being made and category leadership was up for grabs. PRODUCT IMPACT • Shaped AWS AI service direction by identifying gaps between what builders needed and what product teams were building. Contributed to service design, API surface decisions, and launch strategy for Amazon Bedrock and related services. • Identified an unmet market need for practical generative AI education and shipped two products that filled it: 1/ Generative AI on AWS (O'Reilly, 2023): Positioned as the definitive product guide, validated demand through pre-launch signals, and drove measurable platform consideration. 2/ Generative AI with LLMs (DeepLearning.AI, 440K+ learners): Globally adopted generative AI course. Built to give practitioners a practical foundation in generative AI while driving platform awareness and creating a self-sustaining acquisition channel for AWS. • Delivered keynotes and led technical content strategy at major industry conferences, directly contributing to AWS's market leadership positioning during a period of intense competitive pressure. FOCUS AREAS: LLMs, foundation models, prompt engineering, fine-tuning, RAG, RLHF, agents, MCP, A2A, guardrails, Amazon Bedrock.
Drove AI/ML platform adoption across Europe, then expanded to global scope. Defined scalable onboarding and enablement strategies for practitioners, increasing adoption of Amazon SageMaker and the broader ML stack across enterprise and startup segments. • Identified the need for a comprehensive practitioner guide to production ML on AWS and shipped Data Science on AWS (O'Reilly, 2021), a product that established best practices and drove platform credibility with technical decision-makers. • Created "Practical Data Science" specialization with DeepLearning.AI (45,000+ learners), extending the educational product strategy that would later scale 10x with the generative AI course. • Selected as conference chair and keynote speaker at O'Reilly Superstream events, recognized as a leading voice in the AI/ML practitioner community.
In this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, machine learning practitioners, business analysts, data engineers, and data scientists find a practical way to use this exciting new technology. While the focus is on AWS, this book is a great resource for learning generative AI fundamentals and applying these models to real-world applications. * Apply generative AI to your business use cases * Determine which generative AI models to use based on the task * Perform prompt engineering and in-context learning * Fine-tune generative AI models on your datasets * Align generative AI models to human values with reinforcement learning from human feedback * Use techniques like retrieval-augmented generation to augment your model * Explore libraries such as LangChain and React to develop agents and actions * Learn about multimodal models such as Stable Diffusion for image and video generation * Get hands-on with Amazon Bedrock, the AWS generative AI managed service
Developed "Generative AI with large language models" course in collaboration with DeepLearning.AI.
In this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on AWS. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help improve your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.