01 — MLOps Foundations
What is MLOps and why
does it matter in industry?
MLOps (Machine Learning Operations) is the engineering discipline that integrates DevOps,
data engineering, and data science principles to move ML models from experimental development
to operational production in a reliable, reproducible, and monitorable way. In industrial
settings, MLOps solves the "last mile" problem: 87% of ML models built in projects never
reach production (Gartner, 2022).
Sculley et al. (2015), in the seminal paper "Hidden Technical Debt in Machine Learning Systems"
(NeurIPS), document that model code itself represents only 5-10% of the
total production ML system; the remaining 90-95% is data infrastructure, monitoring,
versioning, serving, and lifecycle management. This latent technical debt is a primary cause
of ML project failure in production environments.
🔬
Data Engineering
Versioned data pipelines, quality validation, feature stores
🧪
Experimentation
Experiment tracking (MLflow), model comparison, selection
🚀
CI/CD ML
Continuous integration, automated testing, continuous model delivery
⚙️
Model Serving
REST endpoints, batch inference, TF Lite for edge, industrial APIs
📊
Monitoring
Data drift, concept drift, model performance degradation, alerts
🔄
Retraining
Periodic or drift-triggered retraining, version A/B testing
[1] Sculley D et al. (2015). Hidden Technical Debt in Machine Learning Systems. NeurIPS 2015. doi:10.5555/2969442.2969519
[2] Gartner. (2022). How to Scale AI in Your Organization. Gartner Research ID G00764136.
[3] Kreuzberger D, Kühl N, Hirschl S. (2023). Machine Learning Operations (MLOps): Overview, Definition, and Architecture. IEEE Access, 11, 31866–31879.
02 — Industrial Constraints
Specific Constraints
in Industrial LATAM
Industrial MSME environments in Colombia and LATAM face constraints that make direct adoption
of MLOps architectures designed for large tech companies (Netflix, Uber, Airbnb) inadequate.
OphirIAn identified six critical constraints and corresponding solution patterns:
| Constraint | Manifestation | MLOps solution pattern | Tools |
|---|
| Sparse data |
n<2000 historical process records |
Transfer learning, data augmentation, active learning |
PyTorch, scikit-learn |
| Limited connectivity |
Intermittent or absent internet on-site |
Edge ML inference, offline-first architecture, periodic sync |
TF Lite, ONNX Runtime, MQTT |
| Constrained hardware |
No GPU, low-capacity servers |
Model compression, quantization, lightweight models (LGBM, XGB) |
ONNX, TF Lite, GGUF |
| No in-house ML team |
Operators without ML/statistics training |
AutoML, no-code interfaces, SHAP/LIME explainability |
AutoML frameworks, Streamlit |
| Process drift |
Seasonal changes, raw-material variability |
Active drift monitoring, scheduled retraining |
Evidently AI, Prometheus |
| Regulatory traceability |
INVIMA, CODEX, export certifications |
Model versioning, data lineage, complete audit logs |
MLflow, DVC, Git-LFS |
Shankar et al. (2022), in "Operationalizing Machine Learning in Industrial Settings",
identify that mid-sized industrial organizations with adapted MLOps achieve
74% of models in operational production, versus 13% in organizations
without formal MLOps, with average model-update time of 6 hours vs 3 weeks
in manual systems.
[4] Shankar S et al. (2022). Operationalizing Machine Learning: An Interview Study. arXiv:2209.09125.
[5] Paleyes A, Urma RG, Lawrence ND. (2022). Challenges in deploying machine learning: A survey of case studies. ACM Comput Surv, 55(6), 1–29. doi:10.1145/3533378
[6] Renggli C et al. (2021). Continuous Integration of Machine Learning Models. arXiv:1903.00278.
03 — Technical Stack
Lightweight MLOps Stack
for MSMEs
OphirIAn defined an operationally lightweight MLOps stack, mostly open source, that enables
critical MLOps capabilities (versioning, monitoring, retraining, and serving) with infrastructure
costs below USD 200/month for an average industrial MSME.
Data Layer
Data + Feature Pipeline
- DVC (Data Version Control) for dataset versioning
- Great Expectations for data quality validation
- Apache Airflow light (Astronomer) for orchestration
- InfluxDB / PostgreSQL as a lightweight feature store
- Databricks / Snowflake: excessive for MSMEs
Modeling Layer
Experimentation + Registry
- MLflow Tracking for experiments and metrics
- MLflow Model Registry for model versioning
- Optuna for hyperparameter optimization
- SHAP + LIME for model explainability
- SageMaker / Vertex AI: high cost without large-scale usage
Deployment Layer
Production Serving
- FastAPI / BentoML to serve models as REST APIs
- ONNX Runtime for efficient CPU inference
- TensorFlow Lite for edge deployment (Raspberry Pi)
- Docker for reproducible containers
- Kubernetes: unnecessary complexity at MSME scale
Monitoring Layer
Drift + Performance Monitoring
- Evidently AI for data and concept drift detection
- Grafana + Prometheus for system metrics
- Alerts via PagerDuty or Telegram Bot for operators
- Structured logs with lightweight ELK stack
- Datadog MLOps: prohibitive cost for MSMEs
Early concept drift detection - when input-data distribution changes relative to training
time - is critical in agroindustrial processes with strong seasonality dependence. Lu et al.
(2018) show that MMD-based (Maximum Mean Discrepancy) drift tests detect significant distribution
changes with average latency of 48-72 hours, enabling preventive retraining
before model degradation impacts operational decision-making.
MLOps is not technology only for large enterprises:
it is the lifeline of any production ML model.
[7] Lu J et al. (2018). Learning under Concept Drift: A Review. IEEE Trans Knowl Data Eng, 31(12), 2346–2363.
[8] Chen J et al. (2022). Towards MLOps: A Framework and Maturity Model. Proc. ICSOC 2022. doi:10.1007/978-3-031-20984-0_1
[9] Breck E et al. (2017). The ML Test Score: A Rubric for ML Production Readiness. IEEE BigData 2017.
[10] Zaharia M et al. (2018). Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Eng Bull, 41(4), 39–45.
[11] Symeonidis G et al. (2022). MLOps — Definitions, Tools and Challenges. Proc. IEEE COMPSAC 2022.
04 — MLOps Maturity
Maturity Levels
for Industrial MLOps
Google Cloud defines three MLOps maturity levels (0, 1, 2) that represent the evolution path
from manual ML to full lifecycle automation. OphirIAn adapts this framework to the reality of
Latin American industrial MSMEs, defining a progressive and financially sustainable maturity path.
Level 0
Manual ML
· Model in Jupyter Notebook
· Manual predictions
· No versioning
· No monitoring
· No CI/CD
Risk: Very high
Level 1
Automated Pipeline
· Active MLflow Tracking
· Versioned data pipeline
· REST API serving
· Basic drift monitoring
· Scheduled retraining
MSME Year-1 target
Level 2
Full CI/CD
· Automated ML CI/CD
· Active feature store
· Model A/B testing
· Advanced real-time monitoring
· Auto-triggered retraining
Year 2-3 target
OphirIAn Roadmap for Industrial MSMEs
Month 1-3: Implement MLflow tracking and data versioning (DVC). Establish model metric baselines.
Month 4-6: Deploy serving API (FastAPI + Docker). Enable drift monitoring (Evidently). Operational dashboard (Grafana).
Month 7-12: Automate retraining pipeline. Implement automated alerts. Complete data-lineage documentation.
Year 2+: Full CI/CD with automated model tests. Feature store. A/B testing evaluation for new versions.
[12] Google Cloud. (2023). MLOps: Continuous delivery and automation pipelines in machine learning. cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
[13] Tamburri DA. (2020). Sustainable MLOps: Trends and challenges. Proc. QUATIC 2020. doi:10.1109/QUATIC51189.2020.00016
[14] Makinen S et al. (2021). Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help? Proc. WAIN@ICSE 2021.
[15] Hewage P et al. (2022). Temporal Fusion Transformers for industrial process monitoring. Appl Soft Comput, 128, 109382.
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