Javier Re

Claudia Badell

Actividad en español

¿Cómo tomar mejores decisiones al automatizar? En esta master class vamos a repensar la automatización como parte de una estrategia de calidad, enfocada en el contexto, los riesgos y el trabajo en equipo. Revisaremos prácticas efectivas, errores comunes y criterios para definir qué automatizar, cuándo y por qué, con una mirada más allá de la cobertura y los frameworks.

Nicolás Paez

Actividad en español

Los pipelines son el núcleo de la práctica de CI/CD y resultan fundamentales en el proceso de delivery de cualquier equipo de desarrollo. Actualmente, existe una gran variedad de herramientas para implementarlos, cada una con sus propias características, ventajas y limitaciones. No obstante, más allá de las particularidades de cada herramienta, hay principios comunes que sustentan el diseño de los pipelines y que, con frecuencia, pasan inadvertidos. Al mismo tiempo, no todas las organizaciones son iguales, pero existen ciertos patrones —soluciones reutilizables y probadas— que pueden servir de guía para diseñar e implementar pipelines de forma más simple. En este taller revisaremos los principios clave para el diseño de pipelines y exploraremos cómo distintas herramientas los abordan. También analizaremos los desafíos más comunes que enfrentan las organizaciones al implementarlos y presentaremos algunos patrones que pueden ayudarnos a superar esos obstáculos de manera efectiva.

Tariq King

Actividad en inglés

Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security, and operations. Join Tariq King as he gives you a quality engineering introduction to testing AI and machine learning. You’ll learn AI and ML fundamentals, including how intelligent agents are modeled, trained and developed. Tariq then dives into approaches for validating ML models offline, prior to release, and online, continuously post-deployment. Engage with other participants to develop and execute a test plan for a live ML-based recommendation system, and experience the practical issues around testing AI first-hand. Tariq wraps up the tutorial with a set of expert-recommended, AI engineering practices to help your organization develop trusted machine learning systems.

Dionny Santiago

Actividad en inglés

AI has been rapidly changing the way we approach software testing. Traditional test automation is time-consuming to create and breaks down easily in the presence of change. Thankfully, AI is helping testing teams create less procedural, more resilient tests that are able to self-heal in the presence of modern, rapidly changing, highly dynamic production systems. This sounds great, but you may be asking yourself: How do I get started? What additional skills do I need to learn? What tools are available for me to start using, right now? Join Dionny Santiago as he breaks down different AI techniques and applies them to different testing problems. Discover freely available AI-driven test automation tooling that will help you start building AI-first test automation today without writing a single line of code. Want to tackle the hardest of challenges, and want to learn how to generate new test cases? We will also cover open source tools that can help you build your own neural networks for tackling tough testing problems. Let's build and execute real AI-first tests. No prior programming or AI/ML experience needed!

Javier Re