v0.1-foundation establishes the core MLOps platform scaffold for an end‑to‑end machine learning lifecycle implemented using Databricks Asset Bundles.
This release focuses on reproducible infrastructure, training orchestration, and model lifecycle readiness, forming the base for future inference and monitoring capabilities.
- Infrastructure-as-Code
- Reproducible ML pipelines
- Serverless-compatible execution
- MLflow experiment tracking
- Registry-independent model lifecycle
- Bundle-driven deployment
This project follows a set of architectural principles commonly used in production ML platforms:
-
Infrastructure as Code
All pipeline resources are defined declaratively using Databricks Asset Bundles to ensure reproducibility and environment portability. -
Decoupled ML Lifecycle
Data preparation, training, model selection, inference, and monitoring are implemented as independent pipeline stages. -
Registry Independence
Model selection logic does not assume availability of a model registry, enabling execution in minimal or restricted environments. -
Reproducibility First
Dataset preparation, feature engineering, and model training are designed to produce deterministic outputs from a clean workspace. -
Incremental Platform Evolution
The system is intentionally built in staged releases (v0.1 → v0.3) to mirror real-world ML platform development cycles.
graph LR
A[Prepare Dataset] --> B[Train Model]
B --> C[Select Production Model]
C --> D[Batch Inference]
D --> E[Monitoring & Drift Detection]
subgraph v0.1 Foundation
A
B
C
end
subgraph Future Releases
D
E
end
Batch inference and monitoring are planned for upcoming releases.
Deploy and run the pipeline using the Databricks CLI.
1️⃣ Deploy the bundle
databricks bundle deploy2️⃣ Run the pipeline
databricks bundle run mlops_lifecycle_pipeline3️⃣ Inspect results
Navigate in Databricks:
Workspace → Jobs → mlops_lifecycle_pipeline
Then open MLflow Experiments to view training metrics.
- Databricks Asset Bundle configuration
- Environment isolation
- Job orchestration as code
- Serverless execution compatibility
NYC Taxi dataset ingestion pipeline:
- Public dataset ingestion
- Data quality filtering
- Feature engineering
- Deterministic dataset splits
- Managed Delta tables
Created tables:
main.mlops_lifecycle.train_set
main.mlops_lifecycle.test_set
main.mlops_lifecycle.extra_set
Distributed training workflow:
- Feature vectorization
- Regression model training
- MLflow experiment tracking
- Metric logging:
- RMSE
- MAE
- R²
- Reproducible bundle execution
Registry-independent model loading:
- Stage tagging via MLflow run tags
- Production-stage identification
- Fallback loading logic
- Unity Catalog optionality
Each training run logs:
- Parameters
- Metrics
- Artifacts
- Stage tags
- Execution metadata
Accessible via:
Databricks Workspace → Experiments
The following artifacts demonstrate successful execution of the MLOps pipeline in the Databricks environment.
Example run of the Databricks job orchestrating the pipeline stages.
Training runs logged with parameters, metrics, and artifacts.
Managed Delta tables generated during the pipeline execution.
- Databricks Asset Bundles
- PySpark
- Delta Lake
- MLflow
- Databricks Serverless Compute
- Python
| Stage | Status |
|---|---|
| Data Preparation | ✅ Complete |
| Training | ✅ Complete |
| Model Selection | ✅ Complete |
| Batch Inference | ⏳ v0.2 |
| Monitoring & Drift | ⏳ v0.3 |
Tag: v0.1-foundation
This release establishes the production-ready MLOps scaffold.
- Batch inference pipelines
- Scheduled scoring jobs
- Prediction Delta outputs
- Monitoring & drift detection
- Data quality metrics
- Model performance tracking
Sangam Kumar Singh
Senior Applied AI / MLOps Architect
GenAI • Distributed ML • Decision Intelligence