ML Monitoring & Next Steps
Next Steps & Your MLOps Journey
2 min read
You've completed the MLOps Fundamentals course. Let's recap what you've learned and chart your path forward.
What You've Mastered
Module 1: MLOps Foundation
- MLOps vs DevOps vs LLMOps
- The ML lifecycle from data to monitoring
- MLOps maturity levels (0→4)
- Infrastructure patterns for training and serving
Module 2: Data & Model Versioning
- DVC for tracking datasets and models
- Creating reproducible ML pipelines with
dvc.yaml - Experiment tracking and metrics comparison
- Git + DVC workflow for collaboration
Module 3: ML Workflow Orchestration
- Pipeline concepts and DAGs
- Kubeflow Pipelines for Kubernetes-native ML
- Apache Airflow for scheduling
- Prefect and modern alternatives
Module 4: Feature Stores
- Solving training-serving skew
- Feast for open-source feature management
- Online vs offline stores
- Feature engineering pipelines
Module 5: Model Registry & Serving
- MLflow Model Registry for governance
- BentoML for production serving
- Canary deployments and A/B testing
- Safe rollout strategies
Module 6: Monitoring & Governance
- Data drift detection with Evidently
- Model performance monitoring
- Alerting with Prometheus/Grafana
- ML governance and compliance
Your MLOps Toolkit
┌─────────────────────────────────────────────────────────────────┐
│ MLOps Toolkit │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Versioning Orchestration Features │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ DVC │ │ Kubeflow │ │ Feast │ │
│ │ Git │ │ Airflow │ │ │ │
│ └──────────┘ │ Prefect │ └──────────┘ │
│ └──────────┘ │
│ │
│ Registry Serving Monitoring │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ MLflow │ │ BentoML │ │ Evidently│ │
│ │ Registry │ │ │ │Prometheus│ │
│ └──────────┘ └──────────┘ │ Grafana │ │
│ └──────────┘ │
└─────────────────────────────────────────────────────────────────┘
Common Implementation Paths
Path 1: Startup / Small Team
DVC → MLflow → BentoML → Basic Prometheus
- Simple, minimal infrastructure
- Good for <10 models
- Can run on single server
Path 2: Growth / Mid-Size
DVC → Airflow → Feast → MLflow → BentoML → Evidently
- Scheduled pipelines
- Feature consistency
- Team collaboration
Path 3: Enterprise / Scale
DVC → Kubeflow → Feast/Tecton → MLflow → Kubernetes → Full Observability
- Kubernetes-native
- Multi-team governance
- Compliance-ready
What to Learn Next
Immediate Next Steps
-
Practice Projects
- Build an end-to-end ML pipeline with DVC
- Deploy a model with BentoML
- Set up drift monitoring with Evidently
-
Deepen Your Skills
- Advanced Kubeflow patterns
- Custom feature engineering
- Production debugging
Recommended Next Course
CI/CD for AI/ML Pipelines
Take your MLOps skills to the next level with automated testing, continuous integration, and deployment pipelines specifically designed for ML systems.
You'll learn:
- GitHub Actions for ML workflows
- Automated model testing and validation
- Continuous training (CT) pipelines
- GitOps for ML deployments
- ML-specific CI/CD patterns
Resources for Continued Learning
Documentation
- DVC Documentation
- MLflow Documentation
- Kubeflow Documentation
- Feast Documentation
- BentoML Documentation
- Evidently Documentation
Communities
- MLOps Community Slack
- Kubeflow Slack
- r/MLOps on Reddit
Books
- "Designing Machine Learning Systems" by Chip Huyen
- "Machine Learning Engineering" by Andriy Burkov
- "Building Machine Learning Pipelines" by Hannes Hapke
Key Takeaways
| Principle | Application |
|---|---|
| Version everything | Data, models, code, configs |
| Automate pipelines | Training, testing, deployment |
| Monitor continuously | Drift, performance, business metrics |
| Govern responsibly | Audit trails, bias testing, compliance |
| Start simple, scale gradually | MVP first, then optimize |
Your Action Plan
This Week
- Set up DVC in an existing project
- Register a model in MLflow
- Create a basic drift detection script
This Month
- Build a complete pipeline with orchestration
- Deploy a model with BentoML
- Set up Grafana dashboards
This Quarter
- Implement feature store for a production use case
- Establish governance processes
- Automate CI/CD for your ML workflows
Congratulations on completing MLOps Fundamentals!
You now have the foundation to build, deploy, and maintain production ML systems. The tools and patterns you've learned are used by companies ranging from startups to Fortune 500 enterprises.
Remember: MLOps is a journey, not a destination. Start with what you need, iterate based on real problems, and continuously improve your practices.
Ready for more? Check out our CI/CD for AI/ML Pipelines course to automate your entire ML workflow from code commit to production deployment. :::