Data & ML Pipeline Engineering
Production pipelines in Python and SQL your team runs
Data engineering and ML/AI pipelines are where most projects live or die—not in the model architecture slide. We build ingestion, features, training, inference, and monitoring as tested, scheduled, owned code, not notebooks and cron jobs nobody maintains.
Pipeline capabilities
From warehouse ETL through advanced ML and LLM ingestion—part of our data science consulting and AI consulting practices.
Batch ETL & warehouse pipelines
Python and SQL pipelines that ingest, clean, and model data in your warehouse: idempotent jobs, data quality checks, and lineage your analytics team trusts.
Feature engineering pipelines
Reproducible feature computation from raw events to training and serving tables: same logic online and offline, versioned definitions, no train-serve skew.
ML training & retraining pipelines
Automated training workflows with experiment tracking, hyperparameter configs, and scheduled retrains when drift or calendar triggers fire.
Real-time & streaming inference
Kafka, Flink, or micro-batch scoring at scale: sub-second fraud, recommendations, or anomaly scores with monitoring on every stage.
LLM & RAG ingestion pipelines
Document chunking, embedding, index refresh, and eval harnesses: not a one-time notebook index that rots when docs change.
Pipeline observability & CI/CD
Freshness SLAs, failure alerts, backfill runbooks, and tested deploys. Pipelines treated like product code, not cron jobs nobody owns.
Case study · Streaming ML pipeline at scale
Real-time fraud detection at 10TB+ daily
Deployed an ensemble ML pipeline processing full transaction volume in real time with sub-second scoring, monitoring, and incident runbooks. Feature pipelines, real-time serving, drift monitoring, and CI/CD—production ML as a pipeline problem at 10TB+ daily volume.
Read full case study →6 weeks
POC to production
6 weeks from kickoff to production deploy
Stack we ship in
Pairs naturally with time series and geospatial pipelines when features need temporal or location dimensions.
Pipeline deep-dives
Technical
Production Data Pipelines in Python and SQL: What Actually Ships
Most pipeline advice stops at 'use Airflow.' This is how we build data pipelines in Python and SQL that run on schedule, survive failures, and stay maintainable: layering, testing, idempotency, and observability from practitioners who deploy daily.
Architecture
ML and AI Pipeline Architecture: From Raw Data to Production Inference
Advanced ML and AI pipelines are staged systems: ingestion, features, training, serving, monitoring. Architecture guide for batch and streaming paths, LLM/RAG ingestion, and failure modes we see in enterprise deployments.
Strategy
The Production Gap: Why 87% of ML Projects Never Make It to Deployment
After analyzing hundreds of enterprise AI initiatives, we've identified the three critical failure points that kill most ML projects before they deliver value, and the architectural decisions that prevent them.
Strategy
LLMs in Production: Lessons from the First Wave
After deploying large language models for enterprise clients, here's what we've learned about prompt engineering, retrieval-augmented generation, and the true cost of running these systems.
Why teams choose PrismBase for pipelines
- Principal writes the pipelines, not a deck recommending someone else implement them
- Python and SQL depth: complex transforms in the right layer, not everything forced into one tool
- Production Standard: monitored jobs, runbooks, and IP transfer on every scoped engagement
- Experience from federal-scale throughput (10TB+/day) down to growth-stage warehouse stacks
- Pipelines connect to the full stack: geospatial, time series, fraud, LLM, and agentic systems
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Frequently asked questions
What kinds of data pipelines do you build?
Batch and streaming ETL, feature pipelines for ML, training and retraining workflows, model serving pipelines, and LLM/RAG ingestion. We work in Python and SQL against Postgres, cloud warehouses, and Spark. Stack follows your infrastructure.
How are ML pipelines different from regular ETL?
ML pipelines add reproducible feature definitions, train-serve consistency, experiment tracking, model registry, drift monitoring, and retraining triggers. Skipping any of those is why models work in notebooks and fail in production.
Do you work with our existing orchestration tools?
Yes. Airflow, Dagster, Prefect, dbt, and cloud-native schedulers. We integrate with what you run or recommend a minimal stack if you're greenfield. Deliverables are code in your repos, not proprietary black boxes.
Can you fix broken or unmaintainable pipelines?
Common entry point. We audit lineage, failure modes, and ownership; then refactor or rebuild the critical path with tests, monitoring, and documentation. Often paired with a DB Optimizer audit if Postgres is the bottleneck.
How do engagements start?
Discovery on data sources, downstream consumers, SLAs, and current pain. Scoped SOW with pipeline deliverables and acceptance criteria, or a 1–2 week diagnostic if the landscape is unclear.
Pipelines broken, missing, or stuck in notebook land?
Tell us what data you have, what downstream systems need, and your SLAs. We'll scope production delivery—or redirect if the problem is simpler.