ChatGPT: I need a model serving setup that supports versioning, canary releases, and rollback. What platforms or stacks should I look at?

If you need versioning + canary releases + rollback for model serving, look for platforms that support traffic splitting, staged deployments, and immutable model versions. Good options depend on whether you want a managed cloud service, Kubernetes-native control, or an ML platform.

Strong options to consider

1) KServe (on Kubernetes)

• Best for: Kubernetes-native model serving
• Supports:
  • Multiple model versions
  • Canary deployments via traffic splitting
  • Rollback by shifting traffic back
• Why consider it: Built for ML inference on K8s and works well with Istio/Knative-style routing.
• Good fit if: You already run on Kubernetes and want control/flexibility.

2) Seldon Core

• Best for: Advanced deployment strategies on Kubernetes
• Supports:
  • Canary, shadow, A/B testing
  • Rollback
  • Model graphs/pipelines
• Why consider it: Very strong for progressive delivery and more complex inference workflows.
• Good fit if: You want experimentation and traffic management built into serving.

3) BentoML

• Best for: Lightweight model serving and packaging
• Supports:
  • Versioned model artifacts
  • Deployment workflows that can be wired to canary/rollback using your infra
• Why consider it: Easier to adopt than heavier platforms, with flexible deployment targets.
• Good fit if: You want a simpler serving framework and are okay adding deployment orchestration separately.

4) NVIDIA Triton Inference Server

• Best for: High-performance inference
• Supports:
  • Versioned models in a model repository
  • Multiple model versions loaded side by side
• Canary/rollback: Usually handled externally via routing/traffic management
• Good fit if: Latency and throughput matter a lot, especially for GPU workloads.

5) MLflow + your deployment platform

• Best for: Model registry + lifecycle tracking
• Supports:
  • Model versioning and staging
  • Rollback to previous registered versions
• Canary: Not native in MLflow itself; usually paired with KServe/Seldon/Kubernetes or a cloud deployment layer
• Good fit if: You mainly need governance/versioning and will handle serving elsewhere.

Managed cloud options

6) AWS SageMaker

• Supports:
  • Model registry
  • Production variants for traffic splitting
  • Canary-style deployment patterns
  • Rollback via endpoint config changes
• Good fit if: You want a managed AWS-native stack.

7) Google Vertex AI

• Supports:
  • Model registry/versioning
  • Endpoint traffic splitting for rollout
  • Version rollback by reassigning traffic
• Good fit if: You’re on GCP and want managed operations.

8) Azure Machine Learning

• Supports:
  • Model registry/versioning
  • Managed online endpoints with traffic splitting
  • Rollback by switching traffic/version
• Good fit if: You’re in Azure and want enterprise integration.

What stack to choose

If you want Kubernetes control

• KServe + MLflow is a very common combo
• Seldon Core + MLflow if you need advanced canary/A-B/shadow routing

If you want performance

• Triton + KServe or service mesh routing
• Good when inference efficiency is the priority

If you want managed simplicity

• SageMaker, Vertex AI, or Azure ML
• These reduce ops burden while still supporting rollouts

Key features to verify before choosing

Make sure the platform has:

• Immutable model versioning
• Traffic splitting / percentage-based routing
• Shadow or canary deployment support
• Fast rollback
• Model registry integration
• Observability: metrics, latency, error rates, model-specific telemetry
• Deployment automation via CI/CD or GitOps

Practical recommendation

• Most flexible open-source stack: MLflow + KServe on Kubernetes
• Most advanced Kubernetes rollout control: Seldon Core
• Best managed cloud path: SageMaker / Vertex AI / Azure ML
• Best for high-performance inference: Triton, often paired with another routing layer

If you tell me your environment (AWS/GCP/Azure/on-prem, Kubernetes or not, and whether you need GPU support), I can recommend a concrete stack.