A comprehensive guide to mastering MLflow with Kubernetes, ZenML, and Seldon Core for production MLOps.
This learning path takes you from MLflow fundamentals to building production-grade ML systems running on Kubernetes with integrated orchestration (ZenML) and model serving (Seldon Core).
- ML Engineers looking to production-ize models
- Data Scientists wanting to understand MLOps
- DevOps engineers working with ML systems
- Teams building scalable ML platforms
Get started with MLflow basics, tracking, and model management.
- Core components: Tracking, Projects, Models, Registry
- Installation and setup
- First tracking example
- MLflow UI
- Directory structure and environment variables
- Backend store configuration (SQLite, PostgreSQL)
- Artifact storage (S3, Azure, GCS)
- Autologging for popular frameworks
- Nested runs for complex workflows
- Model signatures and input examples
- Custom metrics and step-wise logging
03 - MLflow Models & Model Registry
- Model registration strategies
- Version management and lifecycle stages
- Loading models from registry
- Model aliasing (MLflow 2.0+)
- Model approval workflows
- Webhooks and notifications
04 - MLflow Deployment Patterns
- Local serving
- Docker containerization
- Cloud deployments (AWS, Azure, GCP)
- Batch inference patterns
- Performance optimization
Scale to Kubernetes and integrate orchestration and serving.
- Complete K8s architecture
- PostgreSQL and MinIO setup
- MLflow tracking server deployment
- High availability configuration
- Training jobs on Kubernetes
- Monitoring and observability
06 - MLflow & ZenML Integration
- ZenML installation and configuration
- Pipeline creation with MLflow tracking
- Advanced pipelines with model registry
- Hyperparameter tuning
- Kubernetes orchestration
- Resource management
07 - MLflow & Seldon Core Integration
- Seldon Core installation
- Deploying MLflow models to Seldon
- A/B testing and canary deployments
- Custom model servers
- Model explainability
- Performance optimization
Build complete production MLOps systems.
- End-to-end architecture
- Configuration management
- Data validation and feature engineering
- Model training with cross-validation
- Automated model registration
- Deployment to Seldon
- Complete pipeline orchestration
09 - Monitoring & Observability
- Prometheus setup and metrics
- Custom instrumentation
- Data drift detection with Evidently
- Grafana dashboards
- Alerting rules and notifications
- Production performance tracking
- Request logging with Elasticsearch
- Multi-model serving and ensembles
- Model routing and multi-tenancy
- Feature store integration (Feast)
- Semantic versioning for models
- GitOps model deployment
- Cost optimization strategies
- Security best practices
11 - MLflow Best Practices ⭐ Must-Read
- Practice 1: Names and Stages
- Practice 2: Model Signatures
- Practice 3: Promotion Gates
- Practice 4: Rollbacks
- Practice 5: Complete Logging
- Practice 6: Model/Policy Separation
- Practice 7: Shadow & Ramp
- Full Detailed Guide
graph TB
subgraph "Development & Training"
A[Data Scientists] --> B[ZenML Pipelines]
B --> C[Model Training]
C --> D[MLflow Tracking]
D --> E[MLflow Registry]
end
subgraph "Kubernetes Infrastructure"
F[PostgreSQL] --> G[MLflow Server]
H[MinIO/S3] --> G
E --> G
end
subgraph "Model Serving"
G --> I[Seldon Core]
I --> J[Production Models]
I --> K[Canary Models]
I --> L[A/B Testing]
end
subgraph "Monitoring"
J --> M[Prometheus]
K --> M
L --> M
M --> N[Grafana]
M --> O[AlertManager]
end
style D fill:#FFD700
style E fill:#FFD700
style I fill:#4A90E2
style N fill:#50C878
- MLflow: Experiment tracking, model registry, and lifecycle management
- ZenML: Pipeline orchestration and MLOps framework
- Seldon Core: Production model serving on Kubernetes
- Kubernetes: Container orchestration
- PostgreSQL: MLflow backend store
- MinIO/S3: Artifact storage
- Prometheus: Metrics collection
- Grafana: Visualization and dashboards
- Evidently: Data drift detection
# Install core tools
pip install mlflow zenml seldon-core
# Kubernetes cluster (minikube, kind, or cloud)
kubectl cluster-info
# Helm for K8s package management
helm versionimport mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
mlflow.set_tracking_uri("http://localhost:5000")
with mlflow.start_run():
iris = load_iris()
model = RandomForestClassifier()
model.fit(iris.data, iris.target)
mlflow.log_param("n_estimators", 100)
mlflow.log_metric("accuracy", model.score(iris.data, iris.target))
mlflow.sklearn.log_model(model, "model")By completing this course, you will be able to:
✅ Set up and configure MLflow for experiment tracking
✅ Manage model lifecycle with MLflow Registry
✅ Deploy MLflow on Kubernetes with high availability
✅ Build reproducible ML pipelines with ZenML
✅ Deploy models to production with Seldon Core
✅ Implement A/B testing and canary deployments
✅ Monitor model performance and data drift
✅ Set up comprehensive observability and alerting
✅ Apply production MLOps best practices
✅ Scale ML systems to handle enterprise workloads
The patterns in this course are used for:
- E-commerce: Product recommendation systems, demand forecasting
- Finance: Fraud detection, credit scoring, algorithmic trading
- Healthcare: Disease prediction, patient risk assessment
- Manufacturing: Predictive maintenance, quality control
- Marketing: Customer churn prediction, campaign optimization
Each lesson includes:
- ✍️ Code examples you can run
- 🏗️ Architecture diagrams
- 🔧 Configuration files
- 🧪 Testing strategies
- 📈 Monitoring setups
Found an issue or want to improve the content? Contributions are welcome!
This learning material is provided as-is for educational purposes.
- Start with 01 - MLflow Fundamentals
- Work through each lesson in order
- Complete the hands-on examples
- Build your own production ML system
- Share your learnings with the community
Ready to become an MLOps pro? Let's get started! 🚀