Artificial intelligence is already changing the way we work, learn, and create. It affects a wide range of professions, not just those in software development or data science. So when you want to work with AI, you need to think beyond just knowing how to code: you also need to understand data, know how a model works, be familiar with business needs, and understand how a tool goes from concept to practical application. It’s this combination of skills that makes AI professionals so in demand today.
If you want to work in AI, you can start right after high school through several types of study programs. There isn’t a single “best AI school” for everyone. Instead, there are multiple pathways depending on your background, career goals, and learning style. Bachelor’s degrees and University Technology Diplomas (BUT) in Computer Science, Data Science, or Mathematics and Computer Science Applied to the Humanities and Social Sciences (MIASHS) provide an excellent foundation, helping you understand data, algorithms, and digital technologies.
From there, you can continue with specialized Master’s degrees in fields such as Data Science, Artificial Intelligence, or Applied Mathematics. These programs allow you to deepen your knowledge and develop advanced expertise. Engineering schools also offer programs with specializations in Big Data or AI, which can be a major advantage if you’re aiming for highly technical roles. If you want to take your expertise even further, some one-year postgraduate Specialized Master’s programs (typically equivalent to a sixth year of higher education) provide an advanced level of specialization.
There are also shorter training options available. These are ideal if you’re looking to change careers, refresh your skills, or combine learning with work experience. Intensive bootcamps, for example, are designed to help you learn quickly through hands-on practice. In just a few months, you can train for careers such as Data Analyst, Data Scientist, or AI Engineer. Accredited online programs are another excellent option if you want the flexibility to learn at your own pace while benefiting from a structured curriculum.
One important point to keep in mind is that work-study programs (apprenticeships) play a significant role in AI and data careers. Many companies actively recruit apprentices in these fields because it allows them to train future professionals using their own tools, datasets, and working methods before integrating them into their teams on a permanent basis.
Training in AI isn’t just about learning a programming language. The first step is building a solid foundation with strong technical skills. Among the most important programming languages, Python is the clear leader. It has become the industry standard for many AI projects. R remains highly valuable for statistics and data analysis, while C++ and Java are useful for optimizing performance or developing more complex technical systems.
However, knowing how to code isn’t enough. You also need to understand machine learning, which is the process of teaching computers to learn from data. In practice, this means understanding how models are trained, how they make predictions, and how to improve their performance. Fundamental algorithms such as linear regression and decision trees are essential concepts to master. You’ll also need to be comfortable with widely used Python libraries like Scikit-learn and Pandas, which are indispensable for data processing and analysis.
As you progress, you’ll move into deep learning, where you’ll work with artificial neural networks and frameworks such as TensorFlow, Keras, and PyTorch. These tools are essential for more advanced applications, including image recognition, text generation, and recommendation systems.
AI careers increasingly require knowledge of Natural Language Processing (NLP) and Generative AI (GenAI). NLP focuses on enabling computers to understand, interpret, and generate human language. Generative AI refers to systems capable of creating content, answering questions, or generating text and images. In this area, understanding Large Language Models (LLMs), Transformer architectures, and prompt engineering has become extremely valuable. Prompt engineering is simply the skill of designing effective prompts that help AI systems produce more accurate and relevant responses.
Finally, it’s important not to overlook MLOps (Machine Learning Operations). This rapidly growing discipline focuses on deploying, monitoring, and maintaining machine learning models in production environments. You can think of it as the bridge between building an AI model and ensuring it operates reliably in real-world applications. Tools such as Docker, Kubernetes, Git, and cloud computing platforms are commonly used in MLOps workflows.
Certifications are a great way to demonstrate your skills in a tangible way. While they don’t replace hands-on experience, they send a strong signal to recruiters and employers. They also help structure your learning by providing a clear, step-by-step progression. When choosing AI certifications, the goal isn’t to collect as many as possible, but to select those that best match your current skill level and career objectives.
a) Cloud provider certifications
Certifications offered by the major cloud providers are among the most recognized in the industry. Recruiters often value them highly, especially for roles where cloud computing, data, and AI are closely connected.
Microsoft Azure offers the Azure AI Fundamentals (AI-900) certification, an excellent starting point for beginners. It’s designed for those with little technical experience who want to understand the fundamentals of artificial intelligence. For more advanced learners, the Azure AI Engineer Associate certification focuses on designing, developing, and implementing AI solutions on the Azure platform.
With Google Cloud, the Cloud Digital Leader certification provides a solid introduction to cloud computing and AI from a business and technical perspective. At a more advanced level, the Professional Machine Learning Engineer certification validates the ability to design, build, and deploy sophisticated machine learning solutions.
On the Amazon Web Services (AWS) side, the AWS Certified Machine Learning – Specialty certification demonstrates advanced expertise in building and deploying machine learning solutions within the AWS ecosystem. For employers, this certification is a strong indicator that you can work with industry-standard tools in professional environments..
b) Academic certifications and online learning platforms
Academic certifications and credentials offered through online learning platforms are also highly valuable, particularly if you prefer learning progressively. The Machine Learning Specialization by DeepLearning.AI, available on Coursera and created by Andrew Ng, is widely regarded as one of the best introductions to machine learning. It’s especially popular because it explains core concepts in a clear, structured, and beginner-friendly way.
Another excellent option is the IBM AI Engineering Professional Certificate, also available on Coursera. This program focuses on practical skills in both machine learning and deep learning, helping learners demonstrate that they can apply AI concepts beyond theoretical knowledge.
Finally, certifications focused on prompt engineering are becoming increasingly relevant as generative AI continues to expand across industries. Knowing how to design effective prompts is now considered a valuable professional skill, particularly for anyone working with enterprise AI tools, large language models, or production AI systems.
Training in AI means building a comprehensive career path: training tailored to your level, useful technical skills, and certifications that strengthen your profile. The most important thing is to move forward step by step and not assume you have to know everything all at once. AI is a broad field, and it has room for a variety of profiles, as long as you have a solid foundation and continue to learn.
And if there’s one thing to remember, it’s that most companies today are looking for work-study students in Data/AI. This is a strong trend because companies prefer to train candidates directly on their tools, data, and work environment. If you want to break into this field, a work-study program can be a great starting point.
Adam de la team STUDCORP.