Turn models into measurable business value - consistently and at scale
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:
Learn more about the MLOps solutions on our blog.
Why us?
We deliver large‑scale data & ML platforms in regulated and high‑throughput environments.
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
- 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
- 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 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
- 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
- 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
- Lineage from data to model to endpoint; immutable versioning
- Approval workflows, role‑based access, policy‑as‑code
- Audit‑ready reports for regulated industries
- Workload tagging, budgets, and cost allocation per team/project
- Autoscaling, spot/preemptible where safe, intelligent cache & IO optimizations
- Continuous cost/performance reviews and recommendations
Engagement model
Discovery & Assessment (2–4 weeks)
current state, gaps, quick wins, TCO
MVP Platform (4–8 weeks)
core CI/CD, registry, serving, monitoring
Scale‑out (ongoing)
add teams, domains, real‑time, governance hardening
Run & Improve
optional managed service with SLOs and cost reviews
Deliverables: architecture & IaC, runbooks, playbooks, dashboards, training for DS/ML engineers, and a roadmap aligned with business priorities
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
No. We support Azure, Databricks, and GCP - including hybrid and multi‑cloud.
Yes. We onboard existing notebooks / scripts into pipelines with tracking, testing, and serving.
Policy‑as‑code, audit trails, RBAC, lineage, and controlled promotions are standard.
Budgeting, tagging, right‑sizing, autoscaling, and regular cost/perf reviews.
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