Turn models into measurable business value - consistently and at scale

We design and implement enterprise-grade MLOps platforms for your data science teams. Build and deploy models faster, operate them safely, and control costs across Google Cloud Platform, Azure and Databricks.

Talk to our expert
See case studies

Why MLOps?

In modern organizations, the challenge is not training a single model - it’s operationalizing dozens of them across teams, environments, and regulatory boundaries. Ad‑hoc scripts, manual handoffs, and “best-effort” monitoring don’t scale.

We build reproducible, governed, and cost‑efficient ML lifecycles so your data scientists can focus on innovation while the platform ensures:

Consistent deployments

standardized CI/CD for models, features, and data pipelines

Observability by default

continuous tracking of data drift, quality, and performance SLAs

Compliance and auditability

versioning, lineage, approvals, and access controls

Cloud efficiency

FinOps guardrails, autoscaling, and right‑sizing from day one

Learn more about the MLOps solutions on our blog.

Our blog

Why us?

We deliver large‑scale data & ML platforms in regulated and high‑throughput environments.

🏦

Banking & fintech

credit scoring and transaction classification on Databricks with full MLOps, API serving, and governance

🛒

E‑commerce

customer data platforms, recommendations, real‑time analytics

🏭

Manufacturing

computer vision at the edge integrated into central MLOps

Our team of platform architects and data engineers has built and scaled enterprise ML platforms across highly regulated and data-intensive industries. We’ve migrated legacy Hadoop clusters to modern AI-ready cloud lakehouses, delivered governed MLOps for financial institutions, and enabled real-time ML for global e-commerce - we know what it takes.

Datumo as the official Partner of Google Cloud, Microsoft Azure, and Databricks combines deep technical expertise with direct access to the latest platform capabilities and best practices. Our approach is pragmatic: we know how to design MLOps that data scientists want to use, and that IT and compliance teams can trust.

Core capabilities

Platform Architecture & Foundation
  • Reference architectures for Azure ML / Databricks / Vertex AI
  • Secure landing zones: private endpoints, firewalls, VNET/VPC peering, IAM policies
  • Data lakehouse integration (Delta/Parquet), catalog, and governance
CI/CD for ML
  • Model, data, and feature pipelines with Azure DevOps, Cloud Build or GitHub Actions
  • Reproducible training with MLflow/Vertex AI Pipelines; artifact and model registries
  • Promotion across dev → test → prod with approvals and automated checks
Feature & Data Management
  • Feature Store (Databricks Feature Store) with online/offline parity
  • Data quality and contracts (Great Expectations, Lakehouse table constraints)
  • Reusable datasets and notebooks with clear ownership and SLAs
Model Serving & Inference
  • Real‑time and batch serving (Databricks Model Serving, Azure ML endpoints, Vertex AI)
  • Scalable API gateways, A/B and canary releases, shadow deployments
  • Hardware acceleration where justified; cost/perf tuning baked in
Monitoring & Reliability
  • End‑to‑end observability: latency, throughput, error budgets, model metrics
  • Data & model drift detection, automatic alerts, retraining hooks
  • Integration with Prometheus/Grafana/Azure Monitor, incident playbooks
Governance, Risk & Compliance
  • Lineage from data to model to endpoint; immutable versioning
  • Approval workflows, role‑based access, policy‑as‑code
  • Audit‑ready reports for regulated industries
FinOps for ML
  • Workload tagging, budgets, and cost allocation per team/project
  • Autoscaling, spot/preemptible where safe, intelligent cache & IO optimizations
  • Continuous cost/performance reviews and recommendations

Reference tech stack

Cloud

Azure, GCP (multi‑cloud ready)

Compute

Databricks, Azure ML, Vertex AI, Kubernetes

Pipelines

Airflow/Cloud Composer, Databricks Workflows, Azure DevOps

Tracking & Registry

MLflow, Vertex AI Model Registry

Feature Store

Databricks Feature Store, Vertex AI Feature Store, Feast

Data Quality

Great Expectations

Streaming

Kafka, Event Hubs, Pub/Sub, Structured Streaming

Observability

Evidently, Prometheus, Grafana, Azure Monitor

Security

IAM, Key Vault/Secret Manager, PE, Firewall, Private Link, BYOK

Engagement model

1

Discovery & Assessment (2–4 weeks)

current state, gaps, quick wins, TCO

2

MVP Platform (4–8 weeks)

core CI/CD, registry, serving, monitoring

3

Scale‑out (ongoing)

add teams, domains, real‑time, governance hardening

4

Run & Improve

optional managed service with SLOs and cost reviews

Benefits

Faster time‑to‑prod

weeks not months, with standardized release trains

Lower run costs

FinOps controls; fewer idle clusters; right‑sized serving

Higher reliability

measurable SLOs, automated rollback, clear on‑call

Compliance‑ready

versioned lineage and approval trails

Efficient DS teams

self‑service environments and guardrails instead of red tape

MLOps Platform Solutions

Top e‑commerce (CEE)

Hadoop → Cloud migration; lakehouse + MLOps at scale
Migrate your existing Hadoop/big data stack into a modern cloud-based lakehouse architecture. Consolidate batch and streaming data into a unified storage/query layer (e.g. Delta Lake, Iceberg) and overlay with fast analytics. On top of that, embed MLOps at scale - continuous model training, experimentation, deployment, drift detection - so that data scientists and ML engineers can push models to production reliably. We also enforce governance, access controls, data lineage, and cost optimization across cloud resources.

Major bank

transaction classification & credit scoring; governed CI/CD and monitoring
Implement intelligent transaction classification models (e.g. categorize payments, incomes, expenses) and credit scoring models (e.g. probability of default, risk tiers) into the bank’s decisioning workflows. Wrap it all in a governed CI/CD pipeline and robust monitoring to satisfy compliance, auditability, and reliability requirements.

Manufacturing

Edge Computer Vision integrated with central registry and automated retraining
Build and deploy a modern computer vision solution for real-time defect detection directly on production lines. Replace unreliable legacy systems and manual, operator-only inspections with automated, edge-based detection. Enable operators to provide direct feedback through intuitive dashboards, feeding verified data back into a central model registry. Automatically retrain and improve models in the cloud, then seamlessly deploy updated versions to all edge devices.

FAQs

Do you only work on one cloud?

No. We support Azure, Databricks, and GCP - including hybrid and multi‑cloud.

We already have models. Can you operationalize them?

Yes. We onboard existing notebooks / scripts into pipelines with tracking, testing, and serving.

How do you handle compliance?

Policy‑as‑code, audit trails, RBAC, lineage, and controlled promotions are standard.

How do you control costs?

Budgeting, tagging, right‑sizing, autoscaling, and regular cost/perf reviews.

Fill out the form and receive the no-obligation consultation

Please enter a valid address email
error alert
Please enter a valid address email
error alert
Please enter a valid phone number
error alert
Thank you! We have received your request and we will contact you as soon as possible.
Oops! Something went wrong while submitting the form.

Have a question or want just to talk?

Maciej Kępa

Data Engineer, Team Leader
Call me
Send me an e-mail

Follow us on social media: