# 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