Introduction to MLOps

1. Introduction to
MLOps
Agus Kurniawan

2. What is
MLOps?
• MLOps (Machine Learning Operations)
is a set of practices that combines
Machine Learning, DevOps, and Data
Engineering to streamline the lifecycle
of ML models—from development to
deployment and monitoring.
Definition:
• Automate, govern, and scale ML in
production.
Key Idea:

3. Why MLOps
Matters

4. ML
Lifecycle vs
MLOps
Lifecycle
• Data → Training → Model → Deploy
(static)
Traditional ML Lifecycle:
• Data → Training → Evaluation →
Deployment → Monitoring →
Retraining → CI/CD → Governance
→ Repeat (continuous cycle)
MLOps Lifecycle:

6. Components of
MLOps
• Data engineering
• Feature engineering & store
• Experiment tracking
• Model versioning
• CI/CD for ML (CI/CD/CT)
• Model registry
• Deployment pipelines
• Monitoring (drift, data & model)
• Governance & documentation

7. Defining
MLOps
• MLOps is the operational backbone for
machine learning systems that
ensures:
• Repeatability
• Reproducibility
• Scalability
• Governance
• Automation across the ML lifecycle
• It brings DevOps concepts into ML, but
with unique ML challenges.

8. Unique
Challenges
in ML (vs
Software
Dev)
Changing data → needs retraining
Model drift
Data quality issues
Feature inconsistency between train vs prod
High compute cost
Complex dependencies (GPU, frameworks)
Long-running training
Hard to reproduce experiments

9. Operational
Challenges

10. Governance
Challenges
• ML requires governance for:
• Bias detection
• Explainability (XAI)
• Auditability
• Regulatory compliance
(GDPR, HIPAA, PDPA)
• Security of data & models

11. What Risks Exist in ML
Projects?
• Data privacy & leakage
• Bias & fairness issues
• Incorrect predictions → business damage
• Drift & model degradation
• Operational downtime
• High compute cost waste
• Unreliable experiments (no reproducibility)

12. MLOps
Mitigation
Strategy
• MLOps reduces risk using:
• Automated Data Validation
• Model Evaluation Gates
• Approval Workflows
• CI/CD/CT Pipelines
• Model Registry with Version
Control
• Observability (logging,
metrics, traces)
• Automated retraining

13. Model Monitoring to Reduce Risk
• Prediction drift
• Data drift
• Model performance drop
• Latency & resource usage
Monitoring includes:
Alerts → rollback → retrain → redeploy.

14. Governance &
Compliance
• MLOps enforces:
• Version history
• Audit trail
• Access control
• Explainability reports
• Reproducibility of experiments
• Risk documentation

15. Scaling ML
Beyond POCs • Most ML starts as small POCs but fails at scale
because:
• Manual processes
• Cannot handle large datasets
• No automated pipelines
• Limited compute resources
• Lack of standardization
• MLOps solves this.

16. What
Scaling
Means
• Scaling ML includes:
• Handling millions of
predictions
• Running parallel experiments
• Supporting multiple
teams/projects
• Orchestrating GPUs/TPUs
• Managing distributed training
• Deploying globally across
environments

17. Scaling
With
MLOps
Tools
• Modern tools for scaling ML:
• Platforms: Azure ML, Vertex AI,
SageMaker, Databricks
• Orchestration: Airflow, Prefect,
Kubeflow
• Model Serving: MLflow, Seldon,
BentoML, FastAPI
• Feature Store: Feast, Tecton
• CI/CD: GitHub Actions, GitLab
CI, Jenkins
• Monitoring: Evidently AI,
WhyLabs, Prometheus, Grafana

18. Scaling Architecture
(High-Level)
• Typical MLOps Architecture
• Data ingestion (Batch/Streaming)
• Feature store
• Model training pipelines
• Model registry
• Containerized deployment
(Kubernetes/Serverless)
• Monitoring + feedback loop
• Automated retraining

19. Example
MLOps Pipeline
Flow
1. Data ingestion
2. Data validation
3. Feature engineering
4. Train model
5. Log experiment
6. Evaluate model
7. Register model
8. Deploy to staging → prod
9. Monitor
10. Trigger retraining

20. Summary
• MLOps makes ML repeatable,
scalable, reliable
• It addresses technical risks and
governance needs
• Scaling ML requires automation,
tooling, standardization
• Successful ML requires continuous
monitoring and retraining