This week I’m wrapping up a continuing education course from Oxford focused on the approaches and tools of the AI Edge Engineering discipline. I’m overwhelmed with how much I’ve learned and how much I still have to learn. Without a doubt, this course will help set the direction of my career moving forward.
I wasn’t new to the concept of AI Edge Engineering. Before entering the course I had already passed my AZ-220 and AI-100. Two certifications I saw as fundamental to practicing AEE on the Azure platform. These certifications focus on Azure’s IoT tools with the AZ-220 and Azure’s AI services and Machine Learning tools with AI-100. It’s important to note that the AI-100 primarily focuses on applying existing AI in solutions. It only covers Data Science from a high level; you might be expected to understand what clustering, categorizing, and regression are, but you aren’t expected to build them from scratch or use any DS tools to build them for the certification test. That’s appropriate for a 100 level certification. Despite having these certifications, I wasn’t ready for the depth we would take on in the course in such a short period.
Luckily, the course used cohort learning as a mechanism to complete some of the more challenging projects. Our group efforts afforded us the opportunity to trade opinions, approaches, and skills to achieve project deliverables. This is also a skill in the AEE field. Few people will have all the skills needed to apply artificial intelligence at the edge. This means that organizations who wish to use AEE will need to team members who have varied specialized skills and knowledge of the bigger picture of AEE. Our projects made this very clear to me.
I won’t go into detail on what we learned and how we learned it, because much of that is the IP of the course and Oxford, but I will say that we dived deep in the following general areas:
- The basic concepts of delivering AI to the edge using IoT and Edge platforms
- Cloud- all the clouds (Azure, AWS, GCS)
- Cloud concepts like PaaS, SaaS, and Cloud Native Services
- All the big pieces of Machine Learning and Machine Learning concepts
- 5G networks
- Device design and development
- DevOps, MLOPS, and even AIOPs
That doesn’t even cover all the guest lectures and insights into AI and AEE application demonstrated. And without the fantastic instruction of course director Ajit Jaokar and his amazing team of tutors and instructors, we wouldn’t have been able to learn so much in such a short period of time. Ajit’s passion for this specialty was clear in every class, which made it a joy to attend. It was definitely worth waking up at 3 AM to attend a class remotely just to spend the time with others who have such a strong appreciation for this burgeoning discipline. This course succeeds because of the people behind it. I have to include the choice of students as well in that success. My study group was full of passionate, knowledgeable, curious, and delightful professionals. We plan to stay in contact well after the course ends. I expect to see amazing accomplishments from their careers.
We wrap up the course on Tuesday and submit all our final homework projects over the next couple of months. I won’t be officially done with the course until May. However, I won’t ever consider myself officially done with AEE, from a learning perspective. I’m taking what I’ve learned from Oxford and building a continuing learning track to master most of the concepts covered in the course.
It’s even clearer to me now that the engineering skills of delivering greater levels of intelligence closer to the source of events and data, so that they may act upon those events and data is a skillset that will be in higher demand in the coming years. I believe this course has helped me to begin building a better roadmap toward mastery of AI Edge Engineering.