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From Curiosity to Credential: My Journey in AI Certification
- X4 Consulting
- 2 days ago
- 4 min read
Artificial Intelligence (AI) has been woven into many aspects of our working, study, and personal lives, whether that is automated tools in the workplace or assistants in apps and search functions we use on the daily.
Despite its growing presence in our lives, AI remains a black box to many. The pace of change adds to the mystery with new tools, updates, and features appearing so quickly that it’s easy to feel left behind.
While keeping up with every development is almost impossible, I found that learning the fundamentals of AI has been an incredibly useful step in understanding the potential and limitations of the many types of AI.
An initial curiosity to understand the fundamentals led me to gain a recognised credential which serves as a strong foundation for understanding our new AI-filled world.
Beginning the Journey
I had already experimented with AI assistants such as Gemini, Claude and Copilot and dabbled with SharePoint Agents and other productivity-focused integrations.
I wanted to understand how these tools actually worked behind the scenes, what makes them “intelligent,” how they process language and data, and whether ‘AI’ is even the right term to describe them. I was also keen to gain knowledge of the how and what of Azure services to better understand it’s use alongside M365 and the modern workplace. Getting hands-on experience of setting up and testing a variety of services myself would be icing on the cake as part of my learning journey. So, with that in mind, I went through the Microsoft Azure AI Fundamentals course and signed up for the exam.
Mastering the Basics to Keep Up with Change
What struck me most during my review of practice materials and past questions prior to my exam was how quickly the content had evolved, with multiple sections removed and added in the space of a year. This continued to run true when looking back on the course to write this blog, with multiple new chapters of interesting content being added over just the last month.
That constant evolution mirrors the pace of change in the AI industry itself. It also emphasises why learning the fundamentals is so important. Once you grasp the underlying concepts like how models are trained, how transformers process information, and which inventions are under the ‘AI’ banner, it becomes much easier to adapt to new tools and frameworks as they appear.
Not Just a Credential
Earning the certification felt like more than just a credential. It gave me a structured foundation to better understand the new technology shaping our world, and a practical way to explore how AI can be responsibly integrated into the work I do.
At X4 Consulting, we see our purpose as helping clients make sense of this change. We can help with translating AI’s potential into practical, people-focused outcomes that drive better decisions and sustainable progress. Get in touch to talk with us about your organisation’s needs.
Learning Microsoft Azure AI Fundamentals - an Overview of What Was Covered
Following a short introduction to the concepts of Generative AI and responsible use of AI, the course takes you through a number of building blocks. Each of these involved hands-on experiences with using the technology through the Azure portal. This course covered:
Machine learning: The use of statistical algorithms which can ‘Learn’ patterns from training data and provide inferences about new data, without the explicit hard-coded instructions of typical data analysis. Machine Learning is the name for the branch for most of our typical AI and is the backbone of Large Language Models (LLM) and other Generative tools.
The difference between and use of simple and effective machine learning models which cover regression, clustering, and binary & multiclass classification.
The advancements which allowed for our current use of AI: from the development of ‘deep learning’ with artificial neural networks, to the creation of Transformer Architecture which allowed for the attention and parallel processing needed for large models to ‘understand’ us.
Generative AI: The most recent evolutionary step from machine learning, which utilises advancements to generate new content, rather than just assessing and predicting data-based relationships.
It was particularly interesting to learn about Large Language models, and how they are able to ‘understand’ language through tokenization, transformers, vector and positional encoding, and attention layers.
Natural language processing: Applications which can “see, hear, speak with, and understand users.” through the use of statistical and semantic language models. In practice, this allows for text analysis, conversation, and translation.
Computer vison: Applications which allow for the processing, analysis and generation of images through the use of convolutional neural networks, regions of interest, and semantic modelling.
It is exciting to think about the possibilities with Automation focused AI tools such as Information Extraction which use computer vision and additional algorithms to extract data from almost any kind of document or image.
What I’ve shared above is really just the tip of the iceberg. The course itself goes much deeper, breaking down each concept, showing how they work, and explaining how everything connects together.
Want to explore how X4 can share its learnings to support your AI adoption and strategy? Get in touch
Full transparency: We used AI to help structure this blog post and refine some wording—practicing what we preach about human-AI collaboration.
