A common mistake I see engineers make: trying to learn AWS, GCP, AND Azure simultaneously.

You end up knowing three platforms poorly instead of one platform well.

My recommendation: pick one, go deep, and be functional in the others.

If you're just starting with ML in the cloud, GCP has the cleanest experience with Vertex AI. For enterprise environments, AWS (SageMaker, Bedrock) is the market leader and what most companies use. For GenAI specifically, Azure has the edge thanks to the OpenAI partnership.

I chose GCP early in my career, got proficient, and then learned enough AWS to navigate it when clients required it. That strategy has served me well.

The fundamental concepts — model training on managed infrastructure, serving endpoints, auto-scaling, cost management — are the same across all three. Once you truly understand one, switching to another takes weeks, not months.

Master one. Be functional in the others. And don't let cloud platform choice become a source of decision paralysis that delays actually building things.