Training Infrastructure & Pipelines • Training Orchestration (Kubeflow, MLflow, Airflow)Medium⏱️ ~2 min
Choosing Your Orchestration Stack: Decision Framework
The orchestration decision reduces to matching tool strengths with your constraints. Airflow style general purpose orchestrators excel when you have diverse data plus ML workflows spanning extract transform load (ETL), feature engineering, and training; strong requirements for backfilling historical date ranges; non containerized or rapidly iterating codebases where 10 minute image build cycles kill productivity; and need for a large ecosystem of integrations with databases, cloud services, and monitoring tools. Thousands of enterprises run Airflow at scale precisely because it unifies heterogeneous workflows under one scheduler with mature operational patterns.
Kubeflow Pipelines and Kubernetes native orchestration make sense when your organization is already Kubernetes first with existing cluster operations expertise; you have heavy distributed training workloads requiring GPU scheduling and autoscaling across dozens of nodes; strict isolation and multi tenancy are mandatory for regulatory or security reasons; and you can invest platform engineering resources into managing image lifecycles, node pools, quotas, and observability. The cost is higher operational complexity and longer feedback loops, but the payoff is first class support for GPU scheduling, heterogeneous runtimes, and horizontal scaling.
MLflow serves as the universal experiment tracker and model registry regardless of orchestration choice. It tracks parameters, metrics, artifacts, and environment manifests for every run; manages model promotion lifecycle with staging and production tags; and provides lineage from raw data through features to deployed model. The pragmatic production pattern is: choose orchestrator based on workflow characteristics as described above, choose compute backend based on scaling and cost requirements, but use MLflow universally for experiment tracking and model governance. This separation means you can migrate orchestrators without losing historical run data or rewriting promotion policies.
💡 Key Takeaways
•Choose Airflow for diverse data plus ML workflows with strong backfill needs, non containerized codebases avoiding 10 minute build cycles, and large ecosystem integration requirements spanning hundreds of data sources
•Choose Kubeflow for Kubernetes first organizations with distributed GPU training at scale, strict multi-tenancy isolation needs, and platform engineering capacity to manage image lifecycles and cluster operations
•MLflow provides universal experiment tracking and model registry independent of orchestration choice, enabling orchestrator migration without losing run history or rewriting promotion workflows
•Hybrid pattern in production: general purpose orchestrator for CPU intensive feature engineering with fast iteration, containerized execution for GPU training with isolation, unified experiment tracker for lineage
•Trade-offs are concrete: Airflow gains iteration speed and backfill maturity but loses GPU scheduling and strict isolation, Kubeflow gains Kubernetes native scaling but adds approximately 10 minutes per iteration overhead
•Team fit matters: small ML teams favor shared environment simplicity and rapid iteration, large platform teams with 10 plus ML engineers benefit from containerized isolation despite DevOps investment
📌 Examples
LinkedIn uses Airflow for orchestrating thousands of daily ETL and feature computation DAGs across diverse data sources due to mature backfill support and operator ecosystem, switches to Kubernetes Jobs for GPU intensive ranking model training, unified by central experiment tracking system
Startup with 3 person ML team chose shared Airflow environment to minimize DevOps overhead and enable sub minute iteration cycles, plans migration to containerized orchestration only after reaching 10 engineers when isolation benefits outweigh operational complexity