Valencia AI, Machine Learning and Computer Vision Meetup

Apr 2, 2026
9 - 11 AM Pacific
Online. Register for the Zoom!

Async Agents in Production: Failure Modes and Fixes

As models improve, we are starting to build long-running, asynchronous agents such as deep research agents and browser agents that can execute multi-step workflows autonomously. These systems unlock new use cases, but they fail in ways that short-lived agents do not.

The longer an agent runs, the more early mistakes compound, and the more token usage grows through extended reasoning, retries, and tool calls. Patterns that work for request-response agents often break down, leading to unreliable behaviour and unpredictable costs.

This talk is aimed at use case developers, with secondary relevance for platform engineers. It covers the most common failure modes in async agents and practical design patterns for reducing error compounding and keeping token costs bounded in production.

About the Speaker

Meryem Arik is the co-founder and CEO of Doubleword, where she works on large-scale LLM inference and production AI systems. She studied theoretical physics and philosophy at the University of Oxford. Meryem is a frequent conference speaker, including a TEDx speaker and a four-time highly rated speaker at QCon conferences. She was named to the Forbes 30 Under 30 list for her work in AI infrastructure.

Visual AI at the Edge: Beyond the Model

Edge-based visual AI promises low latency, privacy, and real-time decision-making, but many projects struggle to move beyond successful demos. This talk explores what deploying visual AI at the edge really involves, shifting the focus from models to complete, operational systems. We will discuss common pitfalls teams encounter when moving from lab to field. Attendees will leave with a practical mental model for approaching edge vision projects more effectively.

About the Speaker

David Moser is an AI/Computer Vision expert and Founding Engineer with a strong track record of building and deploying safety-critical visual AI systems in real-world environments. As Co-Founder of Orella Vision, he is building Visual AI for Autonomy on the Edge - going from data and models to production-grade edge deployments.

Sanitizing Evaluation Datasets: From Detection to Correction

We generally accept that gold standard evaluation sets contain label noise, yet we rarely fix them because the engineering friction is too high. This talk presents a workflow to operationalize ground-truth auditing. We will demonstrate how to bridge the gap between algorithmic error detection and manual rectification. Specifically, we will show how to inspect discordant ground truth labels and correct them directly in-situ. The goal is to move to a fully trusted end-to-end evaluation pipeline.

About the Speaker

Nick Lotz is an engineer on the Voxel51 community team. With a background in open source infrastructure and a passion for developer enablement, Nick focuses on helping teams understand their tools and how to use them to ship faster.

Building enterprise agentic systems that scale

Building AI agents that work in demos is easy, building true assistants that make people genuinely productive takes a different set of patterns. This talk shares lessons from a multi-agent system at Cisco used by 2,000+ sellers daily, where we moved past "chat with your data" to encoding business workflows into true agentic systems people actually rely on to get work done.

We'll cover multi-agent orchestration patterns for complex workflows, the personalization and productivity features that drive adoption, and the enterprise foundations that helped us earn user trust at scale. You'll leave with an architecture and set of patterns that have been battle tested at enterprise scale.

About the Speaker

Aman Sardana is a Senior Engineering Architect at Cisco, I lead the design and deployment of enterprise AI systems that blend LLMs, data infrastructure, and customer experience to solve high‑stakes, real-world problems at scale. I’m also an open-source contributor and active mentor in the AI community, helping teams move from AI experimentation to reliable agentic applications in production.

This workshop provides a technical foundation for managing large scale computer vision datasets. You will learn to curate, visualize, and evaluate models using the open source FiftyOne app.

Date, Time and Location

Apr 8, 2026
10 AM PST - 11 AM Pacific
Online. Register for the Zoom!

The session covers data ingestion, embedding visualization, and model failure analysis. You will build workflows to identify dataset bias, find annotation errors, and select informative samples for training. Attendees leave with a framework for data centric AI for research and production pipelines, prioritizing data quality over pure model iteration.

What you'll learn

• Structure unstructured data. Map data and metadata into a queryable schema for images, videos, and point clouds.
• Query datasets with the FiftyOne SDK. Create complex views based on model predictions, labels, and custom tags. Use the FiftyOne to filter data based on logical conditions and confidence scores.
• Visualize high dimensional embeddings. Project features into lower dimensions to find clusters of similar samples. Identify data gaps and outliers using FiftyOne Brain.
• Automate data curation. Implement algorithmic measures to select diverse subsets for training. Reduce labeling costs by prioritizing high entropy samples.
• Debug model performance. Run evaluation routines to generate confusion matrices and precision recall curves. Visualize false positives and false negatives directly in the App to understand model failures.
• Customize FiftyOne. Build custom dashboards and interactive panels. Create specialized views for domain specific tasks.

Prerequisites:

• Working knowledge of Python and machine learning and/or computer vision fundamentals.
• All attendees will get access to the tutorials and code examples used in the workshop.

April 9, 2026 at 9 AM Pacific
Online. Register for the Zoom

Visual agents that can understand and interact with graphical user interfaces represent a transformative frontier in AI automation. These systems combine computer vision, natural language understanding, and spatial reasoning to enable machines to navigate complex interfaces—from web applications to desktop software—just as humans do. However, building robust GUI agents requires careful attention to dataset curation, model evaluation, and iterative improvement workflows.

Participants will learn how to leverage FiftyOne, an open-source toolkit for dataset curation and computer vision workflows, to build production-ready GUI agent systems.

What You'll Learn:

• Dataset Creation & Management: How to structure, annotate, and load GUI interaction datasets using the COCO4GUI standardized format
• Data Exploration & Analysis: Using FiftyOne's interactive interface to visualize datasets, analyze action distributions, and understand annotation patterns
• Multimodal Embeddings: Computing embeddings for screenshots and UI element patches to enable similarity search and retrieval
• Model Inference: Running state-of-the-art models like Microsoft's GUI-Actor to predict interaction points from natural language instructions
• Performance Evaluation: Measuring model accuracy using standard metrics and normalized click distance to assess localization precision
• Failure Analysis: Investigating model failures through attention maps, error pattern analysis, and systematic debugging workflows
• Data-Driven Improvement: Tagging samples based on error types (attention misalignment vs. localization errors) to prioritize fine-tuning efforts
• Synthetic Data Generation: Using FiftyOne plugins to augment training data with synthetic task descriptions and variations

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

Apr 16, 2026
9 - 11 AM Pacific
Online. Register for the Zoom!

Uncertainty in Large Vision-Language Models and Computer Vision

What if we train a model to classify dogs and cats, but it is later tested with an image of a human? Generally the model will output either dog or cat, and has no ability to signal that the image has no class that it can recognize.

Machine learning models by default do not provide estimates of their confidence or uncertainty, which hinders their use in applications involving humans. Possible solutions is the use of Bayesian Neural Networks or similar models.

In this talk I will show research applications of neural networks with uncertainty quantification, covering Computer Vision, Large Language Models and Vision-Language Models. This includes super-resolution, frame generation, verbalized uncertainty, robustness to corrupted inputs, and input uncertainty.

About the Speaker

Dr. Matias Valdenegro is Tenured Assistant Professor of Machine Learning at the Department of Artificial Intelligence, Bernoulli Institute, University of Groningen since 2022. He studied Computer Science, Autonomous Systems, and Electrical Engineering in Chile, Germany, and Scotland, holding a PhD from Heriot-Watt University on a thesis in detecting marine debris in sonar images. As a Researcher at the German Research Center for Artificial Intelligence in Bremen he conducted research in Computer Vision and Uncertainty Quantification from 2018 to 2022.

Deep Generative Modeling for Multimodal Human Trajectory Prediction

In this talk, I plan to show how deep generative models can be used as powerful multiple-hypothesis predictive models, in human trajectory prediction. This kind of problem arises in particular in autonomous driving. I will show a few works we have done in the past and a few ongoing works in my team.

About the Speaker

Jean-Bernard Hayet studied my engineering degree at Ecole Nationale Supérieure de Techniques Avancées (ENSTA) in Paris, and obtained my master degree in artificial intelligence at University Paris VI. I got my Ph.D. degree from University of Toulouse in 2003, at LAAS-CNRS, in Toulouse.

Cuando el conocimiento es Open la Innovación se acelera

En esta charla mostraremos cómo, cuando el conocimiento es abierto, la innovación se acelera al volverse accesible para cualquier colaborador y no solo para unos pocos expertos. Presentare Promptotyper, una plataforma creada por Innovaitors que integra modelos open source y librerías como LangChain y LangGraph para habilitar soluciones agénticas que guían desde el planteamiento del problema hasta el prototipado.

A través de agentes expertos en innovación, los equipos pueden estructurar retos empresariales y avanzar hacia soluciones en áreas como automatización (por ejemplo con n8n), prototipado de aplicaciones web y analítica de datos. El enfoque democratiza el “saber hacer” innovación en empresas de Latinoamérica, reduciendo la fricción y aumentando la velocidad de aprendizaje y ejecución. Al final, verás cómo convertir el expertise en un sistema reutilizable que escala capacidades de innovación en toda la organización.

About the Speaker

Alejandro Uribe es científico de datos, cofundador de Innovaitors y consultor en industria 4.0. Magíster en Inteligencia Artificial (U. Javeriana), profesor en la U. Externado e investigador en IA en la Javeriana, con 6 años desarrollando soluciones de IA y analítica de datos.

From Using Open Source to Contributing: A Practical Guide to Getting Started

Open source is one of the best ways to learn faster, build real experience, and grow your career, but many people don’t know how to start. In this talk, I share a very practical approach to contributing to open source, based on real experience. We’ll cover how to choose the right project, understand large codebases, start with small contributions, and communicate clearly with maintainers. Using FiftyOne as a real example (but keeping everything general), I’ll show how small actions like fixing docs, improving tooling, or opening a simple PR can lead to long-term impact, visibility, and growth.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow, Docker, and OpenCV. I started as a software developer, moved into AI, led teams, and served as CTO.