What I Learned (and Didn’t) from AWS AI Practitioner Training
- Tanya Zhuk
- Aug 21
- 4 min read
Eight Certificates Later: Was It Worth It?
I didn’t dive into Amazon’s AWS Artificial Intelligence Practitioner Learning Plan because I had dreams of becoming a machine learning engineer. I did it to see what Amazon had to say about two buzzwords reshaping the business landscape: Generative AI and Prompt Engineering.
The program spanned eight modules, each earning me a certificate. That’s eight badges in total are credentials I can show off, but the real question is: what actually matters here for executives, small business owners, and consultants trying to navigate AI’s hype cycle?
My Focus: Generative AI & Prompt Engineering
On generative AI, AWS hit a critical point: are you building a system from scratch, or are you extending something pre-trained? That fork in the road changes everything—from cost to data quality to eventual business use. I found this framing extremely useful, and it’s one I’ve already used in client conversations: build versus enhance.
On prompt engineering, however, the course was underwhelming. The best practices section listed ten or so generic tips (be clear, provide context, etc.), but nothing you wouldn’t get from a medium blog post. Worse, AWS never showed what its own systems respond to best. No templates, no examples. A missed opportunity.
“Prompt engineering isn’t magic. AWS’s advice was mostly obvious—without the templates or examples small businesses really need.”
Where the Program Surprised Me
The module I didn’t expect to value—but did—was on AI security, compliance, and governance. Unlike the fluffy “responsible AI” section, which floated above my head in abstractions about bias and fairness, the governance piece drilled into continuity: who in your organization is actually tracking what the AI has done, where it came from, and how it’s been optimized over time?
“The most valuable insight wasn’t technical at all: every company needs one person accountable for AI governance across the business.”
That struck me. If there’s one role every company should create today, it’s a high-level AI governance lead. Someone who owns not just the tools, but the connective tissue across business units.
Where the Program Lost Me
Two things:
The math-heavy modules. The section on developing machine learning solutions plunged into regression formulas and statistical trade-offs. I’m not a statistician (a word I can barely spell). For a course pitched at “practitioners,” it felt unrealistic to expect execs or small business leaders to absorb that without extra tutoring.
The polish. If your résumé is full of typos, people assume your work will be sloppy. By that same rule, a course riddled with broken links and errors leaves a bad impression. If it wasn’t branded Amazon, I might have stopped reading.
My Advice for Small Business Owners
Before plunging into the deep end of AWS’s universe start with problem-specific tools. Don’t assume you need a full AWS stack to “do AI.”
For businesses at an inflection point, AWS does offer a powerful starting place, especially given the breadth of tools they’ve built. But note the disconnect: their case studies showcase mega-brands spending millions, not small businesses trying to get scrappy. The message is clear: AWS programs are designed for the giants, while smaller brands are left circling at the margins.
That mismatch matters.
“AWS AI Practitioner Training is impressive in scope, but let’s be honest—it was built for mega-brands, not for small businesses.”
What AWS AI Practitioner Training Gets Right—and What Business Leaders Still Need
What AWS Gets Right:
Clear framing around build vs. enhance for generative AI.
Strong governance emphasis: continuity, tracking, and accountability.
Breadth of tools and case studies unmatched by competitors.
What’s Missing:
Role-specific guidance (marketing, general management) instead of dense math for engineers.
Templates, examples, and real-world playbooks for prompt engineering.
Business case studies for small and mid-sized companies, not just mega-brands.
Polish. Broken links and typos signal a lack of care, which undercuts trust.
If AWS called tomorrow asking how to fix this, I’d say: give us case-based tracks for non-technical leaders, provide practical templates, and close the gap between your mega-brand showcase and the reality of smaller businesses trying to scale.
The Certificates I'll Keep, and the Gaps I Won’t Forget
I earned the eight certificates, and in doing so I confirmed what AWS does well—and where it leaves business leaders behind. For me, the value isn’t in the badges; it’s in the perspective: knowing what questions to ask, what frameworks to adopt, and what gaps still exist between the hype and the reality.
If you’re a small business leader or executive, my advice is simple:
Start with small, problem-specific tools.
Learn the language of AI so you’re not flyin
g blind in boardrooms.
But don’t mistake Amazon’s showcase for a map—it’s built for mega-brands, not the rest of us.
I did the homework so you don’t have to. The bottom line? AWS is an impressive technical ecosystem, but it wasn’t designed with you in mind. And maybe that’s the most important lesson of all.
If you want to take the AWS AI Practitioner Training yourself you can find it here along with many other modules that Amazon created for their tools.
My 8 AWS AI Practitioner Certificates earned (screenshots below).
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