# Unifying Your ML Infra

Most ML teams build Frankenstein stacks: Airflow for orchestration, MLFlow for tracking, S3 for storage, Kubernetes for compute. Each tool solves one problem well—until you need them to work together.

Valohai replaces fragmented MLOps tooling with a unified platform that handles orchestration, tracking, storage, and compute without glue code.

## The Cost of Fragmentation

When you stitch together multiple tools, you inherit their collective problems:

**Pipeline failures cascade mysteriously**

* Airflow DAGs fail without propagating context to downstream tools
* Error messages reference internal task IDs instead of ML concepts
* Debugging requires SSH access across multiple systems

**Data lineage evaporates between tools**

* Training outputs land in S3 with no metadata
* Model artifacts lose connection to their training runs
* Reproducing results means archaeology through logs

**Infrastructure becomes everyone's problem**

* Data scientists debug Kubernetes networking
* ML engineers maintain Airflow workers
* Platform teams juggle incompatible tool versions

## The Unified Alternative

Valohai connects every piece of the ML workflow through a single abstraction layer:

**Executions replace scattered jobs**

* Each run tracks inputs, outputs, logs, and metadata automatically
* Failed steps show exactly which data and parameters were used
* Re-running experiments preserves complete lineage

**Pipelines orchestrate without overhead**

* Define DAGs in YAML that version with your code
* Pass outputs between steps without manual wiring
* Monitor progress through one interface, not five dashboards

**Infrastructure adapts to workloads**

* Specify compute requirements per step (GPU type, memory, region)
* Scale from laptops to cloud clusters with the same code
* Pay only for what you use—no idle Kubernetes nodes

## When Unification Matters Most

This approach pays dividends when:

* Your team spends more time on infrastructure than ML
* Reproducing old results requires tribal knowledge
* Onboarding new team members takes weeks of tool training
* Compliance audits demand end-to-end traceability