Component-by-component breakdown with technology choices and rationale.
1. Data Engineering Pipeline
Implementation: Python-based ETL pipelines with Pandas for data manipulation and Pydantic for schema validation.
Data is ingested from multiple sources (CSV files, databases, REST APIs) and transformed into feature vectors. Schema enforcement
occurs at ingestion boundaries to catch data quality issues early.
Technology choices: Pandas was chosen for its mature ecosystem and broad support for data formats. Pydantic provides
runtime type checking and automatic validation, catching schema violations before they propagate through the pipeline. Feature stores
are implemented using Parquet files for efficient columnar storage and fast read performance during training and inference.
Trade-offs: File-based feature stores sacrifice horizontal scalability for simplicity and zero-cost local operation.
For production systems handling high throughput, this would be replaced with Feast or a similar dedicated feature store.
Python
Pandas
Pydantic
Parquet
2. Training Pipeline and Model Management
Implementation: Scikit-learn for model training with MLflow for experiment tracking and model registry. Each training
run logs hyperparameters, metrics, and model artifacts. Models are versioned and tagged (dev, staging, production) to control promotion
through environments.
Technology choices: MLflow provides a complete experiment tracking solution with minimal configuration overhead.
It integrates with scikit-learn's model serialization and provides a REST API for model retrieval. Scikit-learn was chosen for its
simplicity and extensive documentation, though the architecture supports PyTorch or TensorFlow models through MLflow's model flavor abstraction.
Trade-offs: MLflow's file-based backend limits concurrent write performance but enables simple local deployment.
Database-backed tracking would be necessary for teams with high training volumes.
Python
Scikit-learn
MLflow
Model Registry
3. Inference API and Serving
Implementation: FastAPI serves predictions via REST endpoints with automatic OpenAPI documentation. The API loads
models from MLflow's model registry at startup and caches them in memory. Request/response schemas are validated using Pydantic models.
Health and readiness probes enable Kubernetes-style orchestration.
Technology choices: FastAPI provides automatic request validation, serialization, and interactive API documentation.
Its async support enables high-throughput serving. Docker containerization ensures consistent deployment across environments.
Gunicorn with Uvicorn workers provides production-grade process management and graceful shutdown handling.
Trade-offs: In-memory model caching requires sufficient RAM for large models. For production systems with memory
constraints, models could be loaded on-demand or served through dedicated model servers like TensorFlow Serving or Triton.
Python
FastAPI
Pydantic
Docker
Gunicorn
Uvicorn
4. Observability and Monitoring
Implementation: Prometheus client library instruments the FastAPI application with custom metrics (request count,
latency histograms, error rates, prediction distributions). Prometheus scrapes these metrics at configured intervals. Grafana reads
from Prometheus and renders dashboards showing system health, API performance, and model behavior over time.
Technology choices: Prometheus was chosen for its pull-based architecture, which simplifies network configuration and
service discovery. Its time-series database handles high cardinality metrics efficiently. Grafana provides rich visualization capabilities
and alerting integration. Both tools are industry standards with extensive community support.
Trade-offs: Prometheus's local storage limits retention periods. For long-term metric storage, integration with
remote storage systems like Thanos or Cortex would be required.
Prometheus
Grafana
Prometheus Client
Docker Compose
5. CI/CD and Quality Gates
Implementation: GitHub Actions workflows run on each commit, executing linters (flake8, black), type checkers (mypy),
unit tests (pytest), and model evaluation on hold-out datasets. Quality gates fail the build if code quality metrics or model performance
fall below thresholds. Successful builds can trigger automated deployments.
Technology choices: GitHub Actions provides tight integration with the repository and free compute for public repos.
Pytest offers parametrized testing and extensive plugin ecosystem. Black and flake8 enforce consistent code style. Mypy catches type
errors at CI time rather than runtime.
Trade-offs: GitHub Actions has limited compute resources for free tiers. Resource-intensive training jobs would require
self-hosted runners or integration with cloud-based training platforms.
GitHub Actions
Pytest
Flake8
Black
Mypy
6. Infrastructure and Orchestration
Implementation: Docker Compose orchestrates all services (API, MLflow, Prometheus, Grafana) with defined networking
and volume mounts. Services communicate via Docker's internal DNS. Configuration is externalized through environment files.
Technology choices: Docker Compose provides declarative multi-container orchestration suitable for development and
small-scale deployments. It requires no external dependencies beyond Docker itself. For production, this configuration could be
translated to Kubernetes manifests or Helm charts.
Trade-offs: Docker Compose lacks advanced orchestration features (auto-scaling, rolling updates, service mesh).
It is appropriate for development environments but would be replaced with Kubernetes for production deployments requiring high availability
and horizontal scaling.
Docker
Docker Compose
Linux
