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:

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