AWS ML Course Alternative:
Skills That Work Everywhere.

AWS ML courses are designed to prepare you for AWS certifications, not for getting hired as an AI engineer. The curriculum focuses heavily on SageMaker and AWS-specific services. While useful if you work exclusively in AWS environments, most companies use multiple cloud providers or have their own infrastructure. The certification validates your knowledge of AWS products, not your ability to build AI systems that solve business problems.

Certifications can help get past resume filters, but they carry less weight than you might expect. Hiring managers in 2026 care more about demonstrated ability to build and deploy AI systems. A portfolio project showing end-to-end AI implementation often matters more than a certification badge. Many candidates have certifications but struggle in technical interviews because they learned to pass tests, not solve problems.

Cloud-agnostic skills include: Python and core ML libraries (PyTorch, scikit-learn, Hugging Face), understanding ML fundamentals that apply everywhere, containerization with Docker, API development, vector databases, LLM integration patterns, and MLOps principles. These skills transfer across AWS, GCP, Azure, or on-premise deployments. You can learn cloud-specific services quickly once you have strong fundamentals.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

If you are targeting a role at an AWS-heavy company, finishing makes sense. But if your goal is broader employability, consider pausing to build transferable skills. AWS knowledge is not wasted, but it should complement portable AI engineering skills, not replace them. We can assess where you are and create a path that leverages your AWS knowledge while filling gaps in cloud-agnostic fundamentals.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.

Not a waste, but often premature. SageMaker is one tool among many. Learning it before understanding the underlying concepts is like memorizing a recipe without understanding cooking principles. Once you have strong ML fundamentals, picking up SageMaker or any other platform tool takes days, not months. Focus on foundations first, then learn platform-specific tools as job requirements demand.

Yes. Enterprise companies increasingly adopt multi-cloud strategies to avoid vendor lock-in, optimize costs, or meet compliance requirements. Startups often choose based on founder preference or specific service advantages. Even AWS-centric companies integrate with other services. Being cloud-agnostic makes you valuable in more situations than deep expertise in a single provider.