✅ 1. MLflow Local (Laptop)
📌 What it is:
A standalone installation of MLflow on your personal laptop or local development environment using pip install mlflow.
🎯 Use Case:
- Ideal for individuals, students, or experimentation
- Great for learning or building POCs
🔧 Key Features:
- You manually run the MLflow Tracking Server, UI, and register models locally
- Artifacts (models, metrics) are stored on local disk, SQLite, or configured S3/GCS buckets
- You control the backend store, artifact store, and model registry setup
🚫 Limitations:
- No built-in authentication or role-based access
- No multi-user support
- Not scalable for teams or production workloads
- Maintenance and resource allocation is all manual
✅ 2. Databricks Managed MLflow
📌 What it is:
A fully managed, enterprise-grade version of MLflow embedded within the Databricks platform. MLflow is tightly integrated with Delta Lake, Apache Spark, and notebooks.
🎯 Use Case:
- Best for teams, enterprises, and production-grade ML pipelines
- Ideal when you’re already using Databricks for big data, Spark, or data lake management
🌟 Key Features:
- No setup needed — MLflow is part of Databricks workspace
- Integrated model registry with staging/production transitions
- RBAC, authentication, and Unity Catalog for governance
- Auto-logging for popular ML frameworks
- CI/CD pipeline support using MLflow Recipes and Delta Live Tables
- Collaboration features like comments, approvals, and model lineage
🚀 Benefits:
- Highly scalable
- Production-ready
- Seamless data-to-model workflow
✅ 3. Azure ML + MLflow
📌 What it is:
MLflow is natively integrated with Azure Machine Learning (Azure ML) — letting you use MLflow APIs while storing your experiments, runs, and models in Azure ML workspaces.
🎯 Use Case:
- Perfect for teams already on Microsoft Azure
- For projects requiring Azure DevOps, pipelines, or security integrations
🌟 Key Features:
- Supports remote tracking to Azure workspace
- Can log metrics, parameters, and artifacts to Azure Blob Storage
- Model registry is part of Azure ML
- Integrates with Azure Pipelines, Azure Compute, and ML Studio
🚀 Benefits:
- Cloud-native
- Easy to scale
- Secure and auditable
- Backed by Microsoft support
✅ 4. Amazon SageMaker + MLflow
📌 What it is:
MLflow is not natively built into SageMaker, but can be configured to track experiments inside SageMaker notebooks or instances. Artifacts can be stored in S3, and you can use custom endpoints for model deployment.
🎯 Use Case:
- Suitable if you’re on AWS, using SageMaker for training, deployment, and monitoring
🌟 Key Features:
- Run training on SageMaker, log results to MLflow
- Store artifacts in Amazon S3
- Option to deploy models to SageMaker Endpoints
- Track experiments across distributed training jobs
🚀 Benefits:
- Flexible setup
- Can be integrated into AWS CI/CD pipelines
- Scales well with other AWS services
⚠️ Consideration:
- Requires some manual integration/configuration
- Model registry isn’t natively connected
✅ 5. MLflow on Kubernetes (K8s)
📌 What it is:
A self-hosted MLflow setup on top of Kubernetes, usually integrated with tools like Kubeflow, MLRun, or Argo Workflows. You deploy MLflow tracking server, artifact store, and model registry in a K8s environment.
🎯 Use Case:
- Suitable for large engineering teams with DevOps skills
- When you want full control and are managing custom MLOps pipelines
🌟 Key Features:
- Can scale with cluster resources
- Integrated with CI/CD tools (like Tekton, ArgoCD)
- Customizable backend and front-end services
- Deploy tracking servers with persistent volumes, ingress, and secrets
🚀 Benefits:
- Extremely flexible and cloud-agnostic
- Can be customized for any workflow
- Scales automatically with workload
⚠️ Consideration:
- Complex to manage — needs Kubernetes and DevOps expertise
- Requires monitoring, security setup, and cost control
🧠 Summary Table
| Feature | MLflow Local | Databricks MLflow | Azure ML + MLflow | SageMaker + MLflow | MLflow on K8s |
|---|---|---|---|---|---|
| Ideal For | Solo Devs | Teams & Enterprises | Azure-based teams | AWS-based teams | DevOps-Heavy Teams |
| Setup Complexity | Very Low | None | Medium | Medium | High |
| Model Registry | Manual Setup | Built-in | Built-in | Manual | Manual/Custom |
| Access Control | ❌ | ✅ | ✅ | ✅ | Custom/RBAC |
| Collaboration | ❌ | ✅ | ✅ | ✅ | ✅ |
| Artifact Storage | Local/Custom | Managed | Azure Blob | Amazon S3 | Custom (S3/GCS) |
| CI/CD Integration | Manual | Native | Azure Pipelines | CodePipeline | Argo/Tekton |
| Best For | Learning | Full ML Lifecycle | Azure ML users | AWS ML users | Full Control |