Master in Artificial Intelligence, Data and Cloud

The Master in Artificial Intelligence, Data and Cloud is designed to provide you with the knowledge, skills, and tools needed to conceptualize, design, manage, and implement AI projects in real-world scenarios. Its main objectives include:

Understand the current landscape and historical evolution of AI; Analyze the impact of predictive and generative AI on the economy, society, and business models, and understand how it is reshaping products, services, and organizations.

Develop a comprehensive, integrated vision of AI; Gain the ability to confidently apply artificial intelligence in high-value projects, taking into account technological, ethical, strategic, and organizational dimensions.

Master the technical foundations of AI; Learn the core principles of AI—including Machine Learning and Generative AI—as well as key data and cloud technologies, building a solid understanding that will allow you to identify opportunities and address technical challenges.

Recognize the key role of data; Learn to identify, structure, process, and govern the data needed to power effective and sustainable AI solutions.

Design strategies to generate real value with AI; Explore how to achieve tangible impact through AI-driven product design, the management of AI projects, and the alignment of technology with business objectives.

Use cloud technologies as a fundamental AI infrastructure; Master modern cloud platforms to deploy AI solutions at scale and prepare for industry-recognized professional cloud certifications.

Apply generative AI to real-world use cases; Learn to use language models and other generative AI techniques to create innovative solutions across various sectors and contexts.

The Master in Artificial Intelligence, Data and Cloud is designed for professionals and graduates from any discipline who wish to acquire advanced, practical training in Artificial Intelligence. It is especially suitable for:

• Technology and data professionals those who want to specialize in AI, Generative AI, and cloud technologies.
• Innovation and digital transformation leaders professionals seeking to leverage AI as a driver of competitive advantage within their organizations.
• Entrepreneurs and product creators individuals interested in designing intelligent solutions from concept to production.
• Graduates from technical, scientific, or business fields those who aim to enter a job market undergoing rapid technological transformation.

The Master in Artificial Intelligence, Data and Cloud prepares you to take on key roles in the design, development, and management of AI-based solutions in both large organizations and technology startups. Upon completing the program, you will be equipped to lead transformation initiatives and projects centered on artificial intelligence, data, and cloud technologies.

Some of the most relevant career paths include:

• AI Engineer / Machine Learning Engineer Technical profiles capable of designing, training, deploying, and maintaining AI and ML models in production environments, including the use of cloud-based architectures.
• AI Project Manager Professionals able to manage complex AI projects, coordinate multidisciplinary teams, and ensure value delivery aligned with business objectives.
• AI Strategist / Head of AI Strategy Profiles focused on identifying opportunities for AI adoption within an organization, assessing feasibility, designing roadmaps, and aligning initiatives with corporate strategy.
• AI-Focused Product Manager Responsible for defining, designing, and evolving intelligent products that integrate AI, particularly in areas such as generative AI or advanced personalization.
• MLOps / Cloud AI Specialist Experts in integrating AI models into scalable and robust pipelines using cloud tools and platforms.
• Data Analyst Professionals who understand the entire data lifecycle and contribute to generating actionable insights for decision-making. In this master’s program, this role is addressed from an AI-applied perspective rather than as the main career target.
• Data Architect Professionals responsible for designing and implementing scalable and secure data architectures that enable efficient and governed data processing. They work closely with technical and business teams to ensure that the data infrastructure supports the organization’s analytical and AI needs.
• Data & Analytics Consultant Specialists who advise organizations on defining and executing data-driven strategies. They identify improvement opportunities, propose analytical solutions aligned with business goals, and support the implementation of high-impact initiatives.
• Chief Data Officer (CDO) / Data Director An executive role overseeing the organization’s global data strategy. Responsible for leading digital transformation initiatives, driving data governance, and promoting data-driven value creation, innovation, and better decision-making at all levels.

This master’s program develops hybrid professionals with both technical and strategic vision, fully prepared to lead AI adoption across different sectors and organizational contexts.

Foundations of Artificial Intelligence  xplore the evolution of artificial intelligence, its key players, and its ethical and strategic frameworks, with practical workshops on prompting and no-code tools.

AI Strategy  Learn to identify AI opportunities, define use cases, and manage high-impact projects within a strategic framework aligned with organizational goals.

Data Master data management, processing, and architecture, including ETL pipelines, databases, time series, and geospatial data.

Machine Learning Build and evaluate machine learning models applied to real-world problems such as classification, prediction, and segmentation.

Cloud Computing Work with leading cloud platforms to scale AI solutions and prepare for industry-recognized professional certifications.

Generative AI Understand and apply advanced generative models (LLMs, agents, content generation) in business and enterprise contexts.

Deployment and MLOps Learn to deploy and maintain models in production using best practices in MLOps, CI/CD, and monitoring.