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