| | 19 JUNE 2025The most important thing is to focus on the customers, the problems to be solved, and the feasibility of the solutions, always counting on the talent capable of executing ideas to solve these problemsadapted to the different uses our data scientists needed, offering the flexibility to choose instances according to the required characteristics for each task. Having our own cloud account allowed us to manage our costs more effectively, monitoring spending and evaluating the real impact of the solutions implemented.A key piece in accelerating model training and reducing deployment times was the creation of a feature repository. This repository facilitated the reuse of features among different models and teams, significantly speeding up the development process. Additionally, the formation of a dedicated engineering team relied on this repository not only to shorten deployment times but also to strengthen the continuous integration and delivery (CI/CD) pipelines, with unit, performance, and integration tests required to minimize errors. These pipelines not only accelerated the deployment of models but also ensured the quality and reliability of the data needed for inferences.The ability to quickly enable new solutions and correct errors efficiently was greatly enhanced by cloud infrastructure, leveraging services like AWS SageMaker (for feature storage and model registry), serverless functions such as Lambda and API Gateway, and development tools like GitLab. The implementation of these technologies and methodologies allowed Konfío not only to efficiently scale its operations but also to stay at the forefront of implementing data science and artificial intelligence solutions.Artificial Intelligence EraBy the time the wave of Generative AI hit the market, Konfío had already made strides in this field. Various projects have been developed for sentiment and emotion analysis using transcriptions and audio tones, as well as image recognition to contain fraud and the automation of key information extraction from documents to facilitate customer experience.But with this new technology and the exploration of its different variants to identify the most suitable for our problems, we have accelerated their resolution. We used AWS BedRock, as well as OpenAI and other options in content and image generation projects (customized for our clients, but with Konfío's tone) for our Marketing teams, as well as to automate and speed up the audits carried out by operational areas.ConclusionWe have advanced from educated guesses to more complex patterns, from training models with little data locally to requiring cloud instances with GPUs to process data. We do not limit ourselves to a single technology; we are always experimenting. AWS has been useful, but we also recognize the strengths of GCP. The most important thing is to focus on the customers, the problems to be solved, and the feasibility of the solutions, always counting on the talent capable of executing ideas to solve these problems. The technology we use comes afterward. This has been our philosophy for the first 10 years and will continue to be so in the future.
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