MLOps & Data Management

The Problem: ML Systems That Work in Notebooks but Break in Production

A model that performs well in a Jupyter notebook is not a production ML system. Production requires reliable data ingestion, feature consistency between training and serving, reproducible experiments, automated retraining triggers, drift detection, and clear ownership when something fails.

Without this infrastructure, teams spend more time debugging unexplained model degradation than improving model quality. Data scientists cannot reproduce last quarter's best experiment. Engineers cannot tell whether a drop in prediction accuracy is a model issue, a data quality issue, or a pipeline failure.

What We Build

We treat ML infrastructure as engineering discipline, not an afterthought. Every component we design is observable, testable, and owned—so your team can iterate on models without rebuilding the scaffolding each time.

ML Pipeline Orchestration

Reproducible, version-controlled training and evaluation pipelines using Databricks Workflows, Azure ML Pipelines, Vertex AI Pipelines, Argo Workflows, or Prefect—with automated triggers, retry logic, smart step caching, and structured logging throughout.

Feature Engineering & Data Quality

Feature stores—including Vertex AI Feature Store backed by BigQuery—data validation layers using Great Expectations or Soda, and lineage tracking so you always know where your training data came from and whether it is still trustworthy.

Model Deployment & Serving

Containerized model serving on Kubernetes with canary rollouts, A/B testing infrastructure, and latency-bounded SLOs. We handle the path from MLflow model registry to a production endpoint with a real SLA.

Model Monitoring & Drift Detection

Statistical drift detection on input features and prediction distributions—using Vertex AI Model Monitoring, Evidently, or custom TFDV-based pipelines—with alerting that distinguishes training-serving skew from prediction drift, so you know whether to retrain or investigate upstream data quality.

Azure ML & Databricks

Teams on Microsoft's data platform face a specific operational challenge: Azure Data Factory or Databricks handles the data, Azure ML manages experiments, and MLflow tracks models—but connecting these into a governed, production-grade ML system requires deliberate engineering. The components exist; the integration layer that makes them reliable at team scale typically does not.

We work across the full Azure ML and Databricks stack, designing the pipelines, tracking, and governance layer that turns individual services into a coherent ML platform.

• Azure ML Pipelines: We build component-based Azure ML Pipeline definitions with versioned environments and dataset lineage, so training and evaluation steps are reproducible and independently rerunnable. Compute targets are matched to workload—AKS for low-latency inference, AML Compute clusters for training—with cost controls that prevent runaway experiment spending.
• Databricks MLflow & Unity Catalog: We structure MLflow experiment tracking so runs are comparable, reproducible, and linked to the data versions they used. Unity Catalog provides the governance layer—feature tables, model artifacts, and training datasets all carry lineage and access controls, eliminating the undocumented notebook-to-production handoff that breaks most Databricks ML workflows.
• Databricks Workflows & Job Reliability: We engineer Databricks job pipelines with structured retry logic, task dependency graphs, and failure alerting—so a flaky upstream data source does not silently corrupt a downstream model. Delta Live Tables handles incremental feature pipelines where freshness guarantees matter. Job clusters are right-sized and auto-terminated to keep costs predictable.
• MLflow Model Registry & Azure Deployment: We establish model lifecycle governance using MLflow Model Registry as the promotion gate between staging and production. Every Azure ML endpoint deployment traces back to a registered, evaluated model version—with champion/challenger routing configured for safe rollouts. This audit trail is especially important for regulated industries where proving what model is running, and why, is a compliance requirement.

Google Cloud & Vertex AI

Teams invested in Google Cloud's data ecosystem face a specific challenge: BigQuery holds the data, but the path to reliable production ML requires connecting Vertex AI Pipelines, Feature Store, Model Registry, and Model Monitoring into a coherent operational system. Each component works well in isolation. The operational discipline to make them work together—with audit trails, retraining triggers, and governed deployments at team scale—is where most GCP ML initiatives stall.

We work across the full Vertex AI stack and design the integration layer that turns individual GCP services into a production-grade ML platform.

• Vertex AI Pipelines: We build component-based pipeline DAGs using the Kubeflow Pipelines SDK, structured so training, evaluation, and deployment steps are independently versioned, cacheable, and rerunnable. Smart caching means only changed components re-execute—a critical cost control for iterative experimentation. The pipeline becomes the single authoritative record of how a model was built.
• Vertex AI Feature Store & BigQuery Integration: With Vertex AI Feature Store now built natively on BigQuery, feature computation and offline training data live in the same system—eliminating the duplication and training-serving consistency gaps that traditionally break ML pipelines. We design feature pipelines that serve low-latency online requests while using the same BigQuery-backed feature definitions for training, ensuring the data your model trains on matches what it sees in production.
• Vertex AI Model Registry: We establish model lifecycle governance using Vertex AI Model Registry as the control point for versioning, staging, and promoting models through environments. Deployment aliases replace ad-hoc naming conventions—every production endpoint traces back to a registered, evaluated version. This is especially important in regulated industries where you need to prove what model is running and why it was deployed.
• Vertex AI Model Monitoring: We configure monitoring jobs that explicitly separate training-serving skew (input distributions at inference diverging from training data) from prediction drift (model output distributions changing over time). Thresholds are tuned using L-infinity distance and Jensen-Shannon divergence metrics—calibrated to your data, not generic defaults—so your team investigates when it actually matters.

Who This Is For

This works well for teams that are...

• Running ML models in production on Databricks, Azure ML, or Kubernetes with limited monitoring or ownership
• Data science teams whose experiments cannot be reliably reproduced or whose training pipelines break silently
• Engineering organizations scaling from one or two ML models to a multi-model production environment

Might not be the right fit if you are...

• Still in early model experimentation with no production deployment in scope yet
• Looking for model development or data science consulting rather than production engineering

Related Services

MLOps and data infrastructure costs can escalate quickly. Our Cloud FinOps practice specializes in Databricks and Azure cost reduction—often the first thing surfaced by an MLOps audit. For teams deploying models to constrained hardware, see our Edge AI & IoT service. The underlying distributed systems engineering is covered by our Software & Hardware Engineering practice.

Ready to Operationalize Your ML Systems?

Stop firefighting model degradation and pipeline failures. Let's build ML infrastructure that your team can trust in production.