AI Verse
I帽igo Imaz Chac贸n has a diverse work experience in the technology industry. 馃挕 I帽igo began their career at Amadeus IT Group in 2017 as a Business Analyst, where they developed IT solutions for the travel industry and worked as a product owner. In 2021, they joined Airbus Helicopters as a Data Scientist/Software Developer for a short period of time. 馃挕 I帽igo then moved on to Hewlett Packard Enterprise, where they worked as a Software Developer, contributing to the development of a chatbot to assist HPE agents in managing and filtering requests. Currently, they are working at AI Verse as a Software Developer, starting in 2022. Additionally, they worked at ALTEN as both a Data Scientist/Software Developer and a Business Analyst, where they participated in various software development projects, including building an online bank aggregator tool. Overall, I帽igo Imaz Chac贸n possesses solid experience in software development, data science, and business analysis roles.
I帽igo Imaz Chac贸n's education history includes a Master's degree in Data Science from Universitat Oberta de Catalunya, which they started in 2020. Prior to that, they completed a Master's degree in Astronomy and Physics of the Space from Uppsala University from 2016 to 2017. In 2015 to 2016, they spent a year studying Physics and Astronomy as part of an Erasmus program at Uppsala University. 馃挕 I帽igo also pursued studies in Ciencias f铆sicas at Universidad Complutense de Madrid from 2011 to 2016.
In terms of additional certifications, I帽igo obtained the SCRUM Master (PSM I) certification from Scrum.org in February 2019. 馃挕 I帽igo also completed the Machine Learning course offered by Stanford University on Coursera, though the specific month and year of completion are not provided.
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AI Verse
AI Verse offers a self-service image factory that produces high-quality annotated synthetic datasets for the needs of computer vision engineers. An entirely novel process enables the user to describe their ideal dataset and launch its generation on AI Verse鈥檚 render farm, a scalable cloud-hosted cluster of GPU machines, each able to procedurallybuild a 3D scene and render photorealistic images in a few seconds.The dataset builder offers simple but powerful inputs for scene description, lighting and camera placement, emulating the work of a 3D artist in just a few clicks. The user specifies a desired type of environment (e.g. living room, bedroom, office) and chooses object classes of interest from a catalog of 1000+ assets in ongoing expansion. These inputs are enough to procedurally generate any desired number of 3D scenes, respecting user-defined constraints while offering the variations in appearance and content necessary to AI robustness on tasks such as object detection and semantic segmentation. The user can also specify activities to be performed by human agents in the scenes, ranging from simple postures to leisure or work-related activities (e.g. typing on a keyboard, watching TV).A wide range of lighting scenarios can be applied in order to simulate varying weather and time of the day. The placement of the camera is also randomized according to simple and effective constraints that can be defined according to the engineer鈥檚 use case. Sensor parameters (e.g lens parameters, depth-of-field, exposure) can also be adjusted.The process of setting and adjusting the scene and image parameters is made interactive and engaging through the use of live previews, allowing the user to visualize within a few seconds an image rendered on-the-fly on one of our GPU machines, corresponding to the input parameters. Availability of GPU machines for previews is guaranteed though a session-booking system.Once satisfied with their inputs, the user can launch the generation of any desired number of 3D scenes and images captured from those scenes, along with their choice of automatically generated labels (e.g. object 2D/3D boxes, instance masks, depth image). Parallelized rendering on the cloud enables delivery of thousands of images in just a couple hours. The dataset management UI enables the engineer to track progress on any of their dataset orders, and visualize a sample of images from a given dataset as soon as it is available. Once the dataset is completed, the user can download it from the cloud by generating an expiring link whenever needed, which enhances data privacy. The downloadable dataset is presented in a format that makes it ready for AI training and easy to combine with other datasets.