AI Con USA 2026 - Data Engineer
Customize your AI Con USA 2026 experience with sessions covering data engineering.
Tuesday, June 9
AI Deep Dive: Exploring AWS Using Real-World Scenarios
Deepen your AI and machine learning expertise using AWS in an Immersive, hands-on workshop. You’ll use real-world AI challenges while leveraging AWS services like Amazon SageMaker, Bedrock, and Lambda to build and optimize AI-driven solutions. As the session unfolds, new constraints and data anomalies will emerge, mirroring the complexities of real-world AI/ML implementation. Gain insight into how AI solutions perform under evolving conditions, learning to adapt, optimize, and troubleshoot unexpected challenges. Learn the importance of collaboration, strategic thinking, problem-solving,...
Wednesday, June 10
Energy-Efficient AI: Building Sustainable Data Pipelines for the Future
Artificial Intelligence is driving innovation across industries, but its growing energy demands pose critical challenges around cost, scalability, and sustainability. In this talk, Bhanu will share practical strategies for designing energy-efficient AI systems, focusing on: dynamic batching & KV caching for reducing inference overhead, sparse neural networks & structured pruning for lightweight models, carbon-aware scheduling to align compute with renewable energy, federated learning & edge deployments to reduce data transfer energy, and a sustainability maturity model for...
Why Your Agentic PR Never Gets Approved—Going from Vibe Coding to Agentic Engineering
You can write 100% of your code with AI, and only be 15% more productive. Want to know why? Solving the problem of moving faster as an engineer is about way more than code. You design, you problem solve, you discuss and communicate, you test, you shepherd the PR through review, you deploy, and you observe. So why are you being told you should be 10x faster now that AI can write code? David will discuss practical solutions for safely accelerating your entire workflow with AI. He will look at how to collaborate with PM, how to use AI planning, how to ensure validations are solid and builds...
Thursday, June 11
From Prompt to Production: Building AI Systems That Actually Work
AI can now generate code, tests, documentation, and even entire applications in minutes. Yet many organizations are discovering that faster code generation has not translated into faster delivery, higher quality, or greater business impact. Instead, teams are often facing more code churn, more technical debt, and more production incidents. The challenge is no longer getting AI to write code. The challenge is building systems that consistently deliver value to end users. Drawing on real-world experience building AI products, agent-based systems, evaluation frameworks, and production...
Evals Are a Team Sport: Building Scalable Evaluation Pipelines for Trustworthy AI
Modern AI systems fail not only because of flawed models but because evaluation is often treated as a one-time task rather than an ongoing discipline. This session addresses the challenge of scaling evaluation across teams and pipelines to ensure model reliability, fairness, and performance. Drawing from real-world experience in large-scale financial analytics, Anusha Dwivedula will examine how product and data teams can collaborate to design a continuous evaluation framework that integrates precision, recall, and drift metrics with observability, lineage, and quality controls. She will...