Is it worth the 🕕 and 💵?
TLDR; Yes.
Whilst preparing for this exam, I was contemplating whether it’s worth the effort for non-developers to sit this exam and as I went through more of the content I realised even more how not only Microsoft with Azure ML Studio but other Cloud providers like Google with the recently released Vertex AI are making it so easy for us to consume there services and build amazing solutions with ease.
Artificial intelligence and machine learning will play a substantial role in our technological future, and Microsoft definitely is one of those companies at the forefront of that evolution. In particular, Azure has a considerable focus on internal AI and machine learning as well as external growth. Microsoft openly advertises that they want to empower app developers, data scientists, and ML engineers to explore and develop the potential these technologies have.
This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure. Some of the technical areas this exam helps get you familiar with are:
- AI workloads and considerations
- Fundamental principles of machine learning on Azure
- Features of computer vision workloads on Azure
- Features of Natural Language Processing (NLP) workloads on Azure
- Features of conversational AI workloads on Azure
I have a strong belief that AI is not going to be a specific area in our business or software solution, it will be vertically integrated across all levels of the lifecycle. These Systems based on vertical AI are typically full-stack products — meaning the solution is integrated “vertically” from the end-user interface all the way down to the level of data analysis and ML. The design of such systems for professional services firms requires deep subject expertise and industry domain knowledge. Vertical AI systems are usually designed by a collaborative group of industry experts, data scientists and software engineers who are working together to solve specific business problems.
This fundamentals exam will help those not developing, understand what is being done, and whether or not the solution is architected in a responsible way, using the best tools available, within budget and most importantly help understand at a high level what on earth all this ‘AI stuff’ is about! right?🤯
Microsoft outline Responsible AI principles in a very clear way and what they are doing. Moving forward businesses need to ensure they have these principles outlined for their teams and ensure it is working with committees like the Responsible AI Institute. Microsoft put there responsible AI principles into practice through the Office of Responsible AI (ORA), the AI, Ethics, and Effects in Engineering and Research (Aether) Committee, and Responsible AI Strategy in Engineering (RAISE). The Aether Committee advises their leadership on the challenges and opportunities presented by AI innovations. ORA sets the rules and governance processes, working closely with teams across the company to enable the effort.
6 principles of Responsible AI:
Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, Accountability
As some of you may know, there are many services on Azure and AI itself has so many to keep track of: Text Analytics service, Language Understanding service (LUIS), Speech service, Translator Text service, Computer Vision service,Custom Vision service, Face service, Form Recogniser service… the list just keeps going on. After sitting this exam, all these services feel less overwhelming and if someone has a conversation about any one of them, how to make it better?, when to use it?, even if other cloud providers have something similar, it is definitely less daunting/overwhelming now.
So, if you are on the fence about doing this exam, I think it is beneficial to at least go through the training material on the MS Learn website. This will help you keep some of these concepts in the back of your mind next time you are interacting with AI.
Happy Learnings. 👨🏽🏫
