Managed AI Services — SageMaker, Vertex AI & Azure ML
When to use managed vs self-hosted, a cloud-by-cloud breakdown for MLOps teams (Day 23)
By Sandip Das · · 14 min read
💡
Hey — It's Sandip Das 👋
Managed AI platforms like Amazon SageMaker, Vertex AI, and Azure Machine Learning are becoming the default for production ML, and understanding when to use them vs self-hosted stacks (like Kubernetes + Kubeflow) is now a critical architecture decision.
For us, this directly impacts cost, scalability, operational overhead, and time-to-market, making it essential to design efficient, production-grade AI platforms without over-engineering or burning cloud budgets.
Managed AI platforms like Amazon SageMaker, Vertex AI, and Azure Machine Learning are becoming the default for production ML, and understanding when to use them vs self-hosted stacks (like Kubernetes + Kubeflow) is now a critical architecture decision.
For us, this directly impacts cost, scalability, operational overhead, and time-to-market, making it essential to design efficient, production-grade AI platforms without over-engineering or burning cloud budgets.
📚 Key Learnings
- Anatomy of a Managed Platform
- Amazon SageMaker
- Google Vertex AI
- Azure Machine Learning
- The Comparison Matrix
- Managed vs. Self-Hosted
- Hybrid Patterns
- A Decision Framework
🧠 Learn here
There is a moment in the lifecycle of every machine learning team, usually somewhere between the third and the thirtieth model in production, when the seams start to show. The Jupyter noteb
Read the full article on learnxops.com.
Topics: Managed AI Services, SageMaker, Vertex AI, Azure ML, AI, ML, LearnXops Premium, 30 Days of AI