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For many young Indonesians, the path toward a digital career does not always begin from the same starting point. Some come from areas far removed from major technology centers, while others must navigate their learning with limited resources, balancing academic commitments, work, community involvement, and family responsibilities. Yet despite these challenges, the potential is often there from the very beginning. What is needed is access to help unlock and develop it.
Along that journey, Microsoft Elevate Training Center became a learning space for Muhammad Yusuf Fadhel Marwiji from Makassar and Emejleano Rusmin Nggepo from Tangerang Regency to explore AI and cloud technologies through a more practical approach, while gaining a clearer view of the opportunities available in the digital economy.
From Spatial Data to Geospatial AI
Yusuf began his academic journey in the field of forestry. During his studies, he was deeply involved in fieldwork, environmental studies, and Geographic Information Systems (GIS), a discipline that helped him understand how environmental characteristics can be analyzed through location-based spatial data.
This experience sparked his growing interest in technology. For Yusuf, spatial data represents far more than maps or visualization. It is a way to understand the conditions of a particular area through measurable information. In geospatial work, multiple environmental variables, such as elevation, slope, and rainfall, often need to be assessed together. When done manually, the process can be highly time-consuming.
As he began exploring machine learning, Yusuf saw new possibilities. During his master’s research on flood risk, he discovered how machine learning could be applied to identify flood-prone areas by analyzing multiple environmental factors. This led him to seek professional training to gain a more structured understanding of machine learning and AI. Microsoft Elevate came at exactly the right moment.
Yusuf uses machine learning to analyze these variables more efficiently. By processing large datasets and detecting patterns that may be difficult to identify through manual analysis, this technology helps pinpoint areas with a higher risk of flooding. For Yusuf, the benefit of machine learning is not only in the speed of analysis, but also in its ability to help researchers and practitioners draw more informed preliminary conclusions before findings are validated in the field.
“Machine learning helps make work more efficient. Data that previously had to be analyzed one by one can now be processed much faster, especially when dealing with a large number of data points or variables,” said Yusuf.
Beyond machine learning, Yusuf also sees strong potential in automation and cloud technologies to support geospatial work in the future. This training program also broadened his understanding of the Microsoft ecosystem, from Microsoft Excel for tabular data analysis to Microsoft Azure, which he sees as a platform with strong potential to support automation and cloud-enabled workflows.
In the context of research and fieldwork, Yusuf emphasized that analytical results still need to be validated on the ground to ensure that model outputs can be trusted. Looking ahead, he hopes more young people from regions outside major technology centers, including those without an IT background, can gain access to technology training that aligns with evolving industry demands.
Finding New Pathways With the Help of Cloud
While Yusuf saw AI to take his geospatial experience to the next level, Emejleano, better known as Lano, viewed technology as a pathway to building a better future.
A sixth-semester Informatics student at Universitas Sultan Ageng Tirtayasa, Lano did not initially envision himself entering the world of technology. During high school, his family’s economic circumstances meant that he spent more time helping his parents than thinking about plans for higher education. Following the pandemic, his family faced economic hardships and was forced to sell several assets to sustain their livelihood. At the time, pursuing a university degree felt like a difficult goal to reach.
The encouragement to pursue higher education came from his school guidance counselor, who encouraged Lano to apply through the SNBP (National Selection Based on Achievement), Indonesia’s merit-based public university admission pathway. His interest in Informatics grew after discussions with an IT teacher, who showed him how machine learning could support the healthcare sector. From that point on, he began to see technology as something that could create a meaningful impact. Outside the classroom, he pursued practical experience through a Back End Developer apprenticeship, contributed to the tech ecosystem as a Dicoding Community Builder, and founded DiGiat Group, an initiative focused on developing digital products and services.
However, the journey was not always easy. Using an old laptop that frequently slowed down, overheated, or restarted unexpectedly, coding and project work often took much longer than they should have.
“What should have been completed quickly could sometimes take much longer because my laptop would slow down, overheat, or restart on its own. But I always remind myself why I am learning in the first place, for my future and for my family. I cannot let my laptop become a reason to stop,” said Lano.
Through Microsoft Elevate, Lano began to see cloud technology not merely as a concept, but as a new way of overcoming the device limitations he had long faced. Without a GPU on his laptop, running computationally intensive workloads, such as training machine learning models locally, often required far more time than expected. He attempted to work around the issue by upgrading laptop components but still recognized that personal devices have inherent limitations.
After being introduced to Microsoft Fabric and Azure through the Dicoding platform, Lano began to see that learning data and AI does not have to depend entirely on the specifications of the laptop he uses. With a cloud-based approach, processes such as data processing, transformation, model experimentation, and data workflow development can be supported by more flexible computing infrastructure. For Lano, this understanding opened a new perspective: device limitations do not always have to be a barrier to continuing to learn and build technology projects.
One of the experiences that left the strongest impression on Lano was the ETL final project, where he learned how to extract data, clean it, and transform it into a format ready for business analysis or model training. Through this process, he came to understand that AI development does not rely solely on the model itself, but also on the quality of the data that underpins it. To generate meaningful insights, support informed decision-making, and enable AI systems to learn effectively, data must be well-prepared, relevant, and structured. The experience fundamentally changed his perspective on the data engineering profession.
After completing Microsoft Elevate, Lano also began actively using GitHub Copilot in his day-to-day projects. As an AI-powered assistant integrated directly into the coding process, GitHub Copilot helps him by providing contextual code suggestions, allowing him to avoid writing every line from scratch. Tasks that previously required significant time and effort, from initial planning to implementation, can now move forward at a quicker pace.
As a result, Lano is able to focus more on the concepts and logic of the solutions he wants to build, while using AI to help translate ideas into working code that he can further develop and refine. One outcome is a camera- and voice-based application designed to help people who are blind identify objects around them, a project he was able to complete in a relatively short period of time with GitHub Copilot supporting the development process from the outset.
For Lano, access is not simply about having an expensive laptop or a high-speed internet connection. Access also means having the opportunity to learn, discover relevant tools, become part of inclusive communities, and be given the space to experiment and grow.
“For me, access is the key to equality and opportunity. With access, students from any background can have the same opportunity to learn, innovate, and build their future,” he said.
When Access Creates Opportunities
Although Yusuf and Lano came from very different backgrounds, their journeys come to the same conclusion: access to learning opportunities can transform how people imagine their future.
For Yusuf, AI and cloud technologies have opened new ways to strengthen his geospatial work and envision a future for Geospatial Artificial Intelligence (GeoAI) that is more relevant to environmental and industry needs. For Lano, cloud technology has helped narrow the gap between device limitations and the opportunity to learn data, AI, and machine learning with greater confidence.
Their stories are also a reminder that Indonesia’s digital talent does not always emerge from ideal circumstances or access to the best resources. It can grow from a workspace in Makassar, from a modest laptop in Tangerang, from hands-on field experience, from communities, and from the determination to keep finding ways to learn.