Decentralized Data Science on AWS
Discover how to empower data scientists and ML engineers with controlled, self-service infrastructure provisioning using AWS Service Catalog.
About us
We are passionate about the public cloud as well as the DevOps culture and practices!
We believe that the cloud is the new normal and we assist businesses to adopt the public cloud and DevOps practices.
Download the whitepaper: Decentralized Data Science on AWS
This in-depth guide walks through a production-ready architectural pattern that replaces tightly coupled, centralized notebook platforms with standardized, reusable infrastructure products built on AWS CloudFormation.
You’ll learn how to define compliant infrastructure templates, delegate provisioning securely using IAM launch constraints, and enforce governance boundaries across accounts using AWS-native guardrails.
The whitepaper includes architecture diagrams, IAM policy examples, SCP enforcement patterns, and CI/CD integration strategies to help platform teams scale safely.Ideal for cloud architects, platform engineers, DevOps teams, and data leaders operating in regulated or fast-growing environments.
What You’ll Learn?
• How to move from centralized notebook platforms to decentralized, product-based infrastructure provisioning
• How to design Service Catalog portfolios aligned with tagging, security, and cost controls
• How IAM launch constraints protect against privilege escalation
• How to enforce guardrails using AWS Organizations and Service Control Policies
• How to integrate infrastructure templates into CI/CD pipelines for version-controlled governance
• Operational trade-offs and adoption considerations in decentralized environments
Key AWS Services Covered
AWS Service Catalog
Enables controlled self-service provisioning of pre-approved infrastructure products.
AWS CloudFormation
Defines reusable, version-controlled infrastructure templates.
AWS Organizations
Applies governance boundaries across accounts and Organizational Units.
Amazon EC2
Supports custom compute-based notebook and analytics environments.
Amazon SageMaker
Provides managed ML environments when platform abstraction is preferred.
AWS CodePipeline
Automates validation, packaging, and publishing of Service Catalog products.
Ready to Decouple Innovation from Infrastructure Bottlenecks?
Centralized data science platforms simplify governance - but often at the cost of flexibility and resilience.
At Several Clouds, we help organizations transition to a Service Catalog-driven model that delivers standardized deployments, IAM-enforced guardrails, and version-controlled infrastructure - while enabling data teams to experiment independently and securely.
If you're building cloud-native data science environments at scale, this whitepaper outlines the governance framework and architectural patterns we use to help teams move fast - without compromising control or compliance.
Book a meeting
Ready to unlock more value from your cloud? Whether you're exploring a migration, optimizing costs, or building with AI—we're here to help. Book a free consultation with our team and let's find the right solution for your goals.