Deployment & Orchestration

Unified framework to build, ship, and scale AI apps.

Pipeline and orchestration framework for taking ML and LLM systems from development to production.

Kubernetes-native ML/LLM platform.

Kubernetes-based model serving.

Netflix's ML platform for building and managing real-world AI systems. Powers thousands of projects at Netflix, Amazon, and DoorDash. Apache 2.0 licensed.

Kubernetes-native workflow orchestration platform for AI/ML pipelines. Dynamic, resilient orchestration with strong type safety and reproducibility. Used by Lyft, Spotify, and Gojek. Apache 2.0 licensed.

Workflow orchestration framework for building resilient data and ML pipelines. Python-native with modern observability and 200+ integrations. Apache 2.0 licensed.

Cloud-native orchestration platform for developing and maintaining data assets including ML models. Declarative programming model with integrated lineage and observability. Apache 2.0 licensed.

Machine Learning Pipelines for Kubeflow. Platform for building and deploying portable, scalable ML workflows using Kubernetes and Argo. Apache 2.0 licensed.

CNCF graduated container-native workflow engine for orchestrating parallel jobs on Kubernetes. Powers Kubeflow Pipelines and widely used for ML/data processing at scale. Apache 2.0 licensed.

Open-source AI orchestration platform for quickly building and managing continuous ML and generative AI applications across their lifecycle. Automates data preparation, model tuning, and deployment. Apache 2.0 licensed.

Event-driven orchestration and scheduling platform for mission-critical workflows. Infrastructure-as-Code approach with declarative YAML, Git version control integration, and hundreds of plugins for data pipelines and ML workflows. Apache 2.0 licensed.

CNCF open source DevOps tool for packaging, versioning, and securely sharing AI/ML models, datasets, code, and configuration. Packages everything into OCI artifacts stored in existing container registries. Apache 2.0 licensed.

MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle. Reproducible and scalable machine learning workflows on Kubernetes with experiment tracking, model management, and pipeline orchestration. Apache 2.0 licensed.

Netflix's next-generation workflow orchestrator for data and ML pipelines at massive scale. Highly scalable and flexible scheduler designed to handle millions of workflows across thousands of nodes. Apache 2.0 licensed.

Heterogeneous GPU Sharing on Kubernetes. CNCF sandbox project providing GPU virtualization, slicing, and scheduling for efficient AI workload management across heterogeneous accelerators (GPUs, NPUs, MLUs). Apache 2.0 licensed.

Kubernetes-native GPU scheduler for AI workloads at large scale. Originally developed by Run:ai, now open-sourced by NVIDIA. Optimizes GPU resource allocation with dynamic allocation and efficient queue management. Apache 2.0 licensed.

Infrastructure automation tools for building GPU clusters with Kubernetes and Slurm. Deploys multi-node GPU clusters with monitoring, logging, and storage for AI/HPC workloads. BSD-3-Clause licensed.

Run, manage, and scale AI workloads on any AI infrastructure. Unified interface to access and manage compute across Kubernetes, Slurm, and 20+ cloud providers. Used by Shopify and research institutions for training and inference. Apache 2.0 licensed.

Cloud-native batch scheduling system for compute-intensive workloads. CNCF incubating project with gang scheduling, job dependency management, and topology-aware scheduling for AI/ML and deep learning. Apache 2.0 licensed.

Kubernetes resource scheduler for batch, data, and ML workloads. Provides hierarchical resource queues, multi-tenancy fairness, and gang scheduling for big data and machine learning applications. Apache 2.0 licensed.

Kubernetes-native job queueing system for batch, HPC, AI/ML, and similar applications. Cloud-native job queueing with resource flavor fungibility, fair sharing, cohorts, and preemption policies. Integrates with Kubeflow, Ray, and JobSet. Apache 2.0 licensed.