> For the complete documentation index, see [llms.txt](https://docs.valohai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.valohai.com/readme/philosophy/infra-abstraction-for-ds.md).

# Let Data Scientists Be Scientists

Data scientists shouldn't wrestle with cloud permissions, network configurations, or storage mounts. That's infrastructure work, not science.

Valohai inverts the traditional model: your ops team owns the environments, your data scientists own the experiments. Whether you're on AWS, Azure, GCP, or on-premises, Valohai abstracts these details away.

## The Problem with Traditional Platforms

Most ML platforms force data scientists to become part-time DevOps engineers. They demand cloud credentials, network configurations, and security policies before you can run a single experiment.

This approach breaks down because:

* Data scientists waste time on infrastructure instead of model improvement
* Security risks multiply when everyone needs cloud access
* Onboarding takes weeks instead of hours

## How Valohai Works Differently

### Zero Cloud Credentials for Data Scientists

Your data science team never touches:

* Cloud provider CLIs or authentication tokens
* Virtual networks, subnets, or firewall rules
* Identity management or permission policies
* Storage bucket configurations or access keys

Instead, they select pre-configured environments and run experiments.

### Clear Separation of Concerns

**Infrastructure Team Handles:**

* Environment setup and maintenance
* Cloud resource provisioning
* Security policies and access controls
* Cost optimization and monitoring

**Data Science Team Focuses On:**

* Experiment design and execution
* Model architecture and hyperparameters
* Data preprocessing and feature engineering
* Results analysis and iteration

## The Outcome

This separation delivers concrete benefits:

* **Faster onboarding**: New team members run experiments on day one
* **Better security**: Cloud credentials stay with the ops team
* **Higher productivity**: Data scientists spend 100% of their time on ML problems
* **Controlled costs**: Centralized environment management prevents resource sprawl

## Implementation in Practice

Here's how a data scientist runs an experiment:

```shell
vh execution run --environment production-gpu
```

Behind that simple command, Valohai handles:

* Provisioning the right compute instance
* Mounting data stores with proper credentials
* Injecting secrets and configuration
* Setting up monitoring and logging

The data scientist sees none of this complexity. They get results, not infrastructure headaches.

## When to Use This Pattern

This approach works best when:

* Your team has dedicated infrastructure/platform engineers
* Security and compliance matter
* You want to scale beyond a handful of researchers
* Cloud costs need active management


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.valohai.com/readme/philosophy/infra-abstraction-for-ds.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.