The Short Answer
MLOps consulting services give an enterprise the systems architecture, engineering capacity, and governance evidence it needs to deploy, scale, and monitor machine learning models in production. In practice that means an MLOps architecture assessment, automated CI/CD-CT pipelines built on feature stores, high-throughput inference serving (NVIDIA Dynamo-Triton, vLLM), and model risk controls built into the release process, aligned to the regulators you answer to, from the MAS Guidelines on AI Risk Management in Singapore to APRA, PRA, and US banking guidance. Vinova delivers these enterprise MLOps services through dedicated engineering pods that work inside your repositories.
Moving a machine learning model from a notebook into mission-critical production is one of the highest-friction problems in enterprise engineering.
Teams routinely build an accurate predictive model or a generative AI prototype in a sandbox, then stall at the production boundary. Four failure patterns account for most of the stalls:
- The handoff void: Data scientists pass notebooks to backend developers, who spend months re-implementing data transformations in another language. Small differences create training-serving skew, and the model behaves differently live than it did in validation.
- The silent collapse: The model sits behind a REST endpoint that reports healthy uptime while drift quietly erodes its accuracy. Nothing alerts, because nothing was built to watch the predictions.
- The compute sinkhole: Naive Python serving wrappers hit concurrency limits and run out of GPU memory under load, which pushes teams to over-provision GPU instances that sit idle off-peak.
- The supervisory exposure: In regulated markets, running probabilistic models without continuous validation, fairness checks, and traceable lineage leaves you unable to answer a supervisor’s first question. MAS issued its Guidelines on AI Risk Management on 7 October 2026, and comparable expectations apply in Australia, the UK, and the US.
The question for a CTO or CRO is not whether your models are in production. It is whether you can ship, scale, and defend them without a heroic effort at every release.
Vinova’s enterprise AI, MLOps, and model governance practice exists to answer that question. We do not sell slide decks or vendor-locked SaaS suites. We deploy dedicated, full-stack engineering pods that work inside your code repositories, build infrastructure inside your own cloud perimeter, automate the continuous training gates, and produce the evidence your risk function needs.
Key Architectural and Commercial Takeaways
1. Engineers, not memos: Our pods write production code in your repositories, under your merge control. Pipelines, feature stores, serving runtimes, and drift monitors ship as working systems rather than as a report.
2. Four automated gates: A candidate model reaches production traffic only after it clears statistical, fairness, serving, and lineage gates in CI/CD. You set the thresholds, and the pipeline enforces them.
3. Governance as a pipeline output: Lineage, fairness results, drift telemetry, and test reports are generated by every release, so audit readiness becomes a by-product of engineering instead of a project.
4. A modeled 36-month ledger: In our modeled scenario, a four-engineer in-house MLOps team costs about $2.38M, a project agency about $1.49M, and a dedicated Vinova pod about $0.74M. Section 5 lists the assumptions and what the model leaves out.
Here is the operational blueprint of Vinova’s enterprise MLOps and model governance services: the four practice pillars, the regulatory baselines we engineer to, the modular stack we deploy, the four-gate promotion pipeline, pod sizing and cost, and the governance model behind every engagement.
Table of Contents
1. MLOps Consulting Services: The Four Practice Pillars
Engagement Rule of Thumb: Do not pay consultants to write strategy documents while your engineers fight production fires. Engage specialists who write the code, configure the pipelines, and leave a working platform inside your repositories.
Concrete systems, not abstract advisory memos.
Our MLOps consulting services run across four pillars. Use the first to find out where you stand, and the other three to fix it.
The Four Pillars of Vinova’s Enterprise MLOps Services
| Pillar | Engagement shape | What you receive |
|---|---|---|
| 1. MLOps architecture assessment | 2 to 4 weeks | A 20-point scorecard, bottleneck diagnosis, a GPU FinOps profile, and a 90-day roadmap. |
| 2. Production pipeline and platform engineering | Dedicated pod | A feature store, containerized CI/CD-CT pipelines, a model registry, and canary rollout. |
| 3. High-throughput inference and FinOps | Dedicated pod | An optimized serving runtime, batching and quantization, and GPU autoscaling. |
| 4. AI model governance and risk engineering | Dedicated pod, alongside your risk team | Promotion gates, lineage, fairness and drift controls, and automated test evidence. |
Pillar 1: MLOps Architecture Assessment and Maturity Roadmap
If you run models in production that release slowly, decay silently, or burn cloud budget, an MLOps architecture assessment is the fastest way to find out why. It is a systems audit run by engineers who have built the fix:
- The 20-point CTO scorecard: We score your capabilities across data and feature operations, pipelines and GitOps, serving and FinOps, and governance and telemetry, then place you on Vinova’s five-level MLOps maturity model, which extends the three-level Google and five-level Microsoft frameworks.
- Bottleneck isolation: We profile your ingestion layers to find training-serving skew, unversioned SQL dependencies, and non-linear notebook execution.
- GPU FinOps profiling: We inspect GPU memory allocation and tensor core activity through NVIDIA DCGM metrics to find where idle silicon is inflating your cloud invoice.
- Deliverable: An architecture audit report and a stage-gated 90-day roadmap that defines your Thinnest Viable Platform (TVP), the smallest platform that stabilizes your systems. The term comes from the Team Topologies model of platform teams.
Pillar 2: Production Pipeline and Platform Engineering
We build the self-service platform that lets your data scientists and ML engineers ship models without filing infrastructure tickets:
- Feature stores: Feast feature definitions that serve point-in-time-correct historical joins from your offline store (Snowflake, BigQuery, or S3 with Parquet) for training, and low-latency lookups from an online store (Redis or DynamoDB) for inference, so training and serving read the same definitions.
- Containerized CI/CD-CT pipelines: Declarative pipelines in Argo Workflows or Kubeflow Pipelines, where each step runs in an isolated container with explicit resource limits.
- Continuous training triggers: Retraining that starts on label volume thresholds, sliding-window drift alerts, or performance-drop webhooks rather than on someone’s calendar.
- Model registry: MLflow or a cloud registry that links every artifact to its dataset hash and Git commit.
Pillar 3: High-Throughput Inference and FinOps Optimization
Gartner expects inference to account for at least 70% of a model’s lifetime cost, so the serving runtime is where optimization pays back first. We tune it for throughput and cost:
- Dedicated serving runtimes: NVIDIA Dynamo-Triton (formerly Triton Inference Server) and vLLM replace hand-built Python web wrappers (FastAPI or Flask) with runtimes designed for batching, concurrency, and GPU memory management. They also remove the Python GIL contention that throttles naive wrappers.
- Dynamic and continuous batching: The runtime groups requests that arrive within a configurable queue delay into one GPU operation. The gain depends on your model and your traffic mix, so we benchmark on your workload before we quote a number.
- Paged KV-cache memory: vLLM’s PagedAttention manages the transformer key-value cache in pages. Its authors report cutting memory waste from 60 to 80% to under 4% and improving throughput by 2 to 4 times over earlier systems, which also reduces out-of-memory crashes under concurrency.
- Quantization: INT8, FP8, or 4-bit AWQ to fit larger models on fewer GPUs, validated against your accuracy gates before rollout.
- Event-driven autoscaling: KEDA scales GPU worker deployments on queue depth in Kafka or Redis, down to zero when traffic stops. Scaling to zero trades cost for cold-start latency (node provisioning and model load), so we set it per workload.
In the workload we modeled in our AI cost analysis, an optimized serving stack reduced the 36-month total by 61% to 70%. Treat that as a modeled result, and let the assessment produce the figure for your own estate.
Pillar 4: AI Model Governance Consulting and Model Risk Engineering
Our AI model governance consulting turns regulatory expectations into controls that an engineer can test and an auditor can inspect:
- Gates in code: Validation, fairness, and lineage checks run in CI/CD and block promotion on failure, with segregation of duties enforced in the model registry so developers cannot approve their own models.
- Fairness assertions: Veritas-style assessment methods, using the metrics and thresholds your validation team documents for each use case.
- Test evidence: IMDA AI Verify tests, Project Moonshot for generative AI, and your own validation suites run inside the pipeline, so every candidate produces a report.
- Generative AI controls: NeMo Guardrails on inputs and outputs, plus retrieval evaluation (TruLens groundedness, Ragas faithfulness) with thresholds you set per use case.
- Production-data boundary: Regulated customer data stays inside your cloud perimeter (for example AWS ap-southeast-1 in Singapore or ap-southeast-2 in Sydney), while our pods work on masked or synthetic test data.
Our AI model risk management guide for Singapore covers the framework behind this pillar in depth.
2. Model Risk Management Services Singapore and Beyond: The Baselines We Engineer To
Regulatory Rule of Thumb: Engineer to the strictest regime you answer to, record which one it is, and make the pipeline produce the evidence. Never cite a rule you have not checked.
Regulators use different words for the same four expectations: an inventory, independent validation, monitoring, and traceability.
Our model risk management services in Singapore are built around the MAS Guidelines on AI Risk Management. The same pipeline controls carry over to the other regimes our clients report under:
Regulatory Baselines for Production AI by Jurisdiction
| Jurisdiction | Instrument | Status | What it means for your platform |
|---|---|---|---|
| Singapore | MAS Guidelines on AI Risk Management (issued 7 October 2026), MAS information paper on AI model risk management (December 2024), and the FEAT Principles (2018) | Supervisory guidelines applied proportionately, phased from 7 October 2027 and 7 October 2028; good practices; principles | An AI inventory with materiality ratings, lifecycle controls, independent validation, and accountability for third-party AI. |
| Singapore | PDPA, MAS technology risk guidelines, and the MAS cyber hygiene notice | Law, guidelines, and binding notices | Comparable protection for personal data sent overseas (section 26), secure development, access control, and multi-factor authentication for administrator accounts. |
| Australia | APRA CPS 230 Operational Risk Management (effective 1 July 2025) | Prudential standard | A register of material service providers, fourth-party risk management, and contract safeguards for services that support critical operations. |
| United Kingdom | PRA SS1/23 Model Risk Management Principles for Banks (in effect since 17 May 2024) | Supervisory statement for banks with internal model approval | Model identification and risk classification, governance, independent validation, and risk mitigants, including a principle on AI and machine learning models. |
| United States | Revised interagency Guidance on Model Risk Management from the OCC, Federal Reserve, and FDIC (April 2026), which replaced SR 11-7 and OCC Bulletin 2011-12 | Non-enforceable guidance | Risk-based oversight, effective challenge, and ongoing monitoring. Generative and agentic AI sit outside its scope, so broader governance practices apply. |
These instruments change quickly, and this table is an engineering orientation rather than legal advice. Confirm the obligations that apply to your licence type against each regulator’s text.
A Citation to Correct: MAS TRM Section 10
Many teams cite “MAS TRM Section 10” as the rule that governs machine learning models. Section 10 of the 2021 MAS Technology Risk Management Guidelines covers cryptography. The TRM Guidelines still matter for AI, because their expectations on secure development, access control, third-party risk, and cyber operations apply to the systems that train and serve your models, but the model-specific expectations sit in the AI Risk Management Guidelines and the December 2024 information paper. Cite TRM by topic, and cite the AI guidance for the model lifecycle.
3. Enterprise MLOps Services Architecture: The Modular Stack We Deploy
Architectural Rule of Thumb: Do not lock your enterprise into a proprietary SDK that prevents you from moving clouds. Assemble modular open-source components on Kubernetes behind standard API contracts.
Modular systems protect your sovereignty and your negotiating position.
We design cloud-agnostic platforms from open-source components and run them on the Kubernetes environment you already operate: Amazon EKS, Google GKE, Azure AKS, or on-premise clusters. We do not require a proprietary control plane. The platform has five layers, each with a defined hand-off to the next:
The Vinova Enterprise MLOps Systems Topology
| Layer | Components | Hand-off |
|---|---|---|
| 1. Data and feature state | Snowflake, BigQuery, or Delta Lake on S3; Feast offline and online stores; Redis; Great Expectations data contracts | Versioned features and dataset hashes |
| 2. Orchestration and compute | Argo Workflows or Kubeflow Pipelines; Ray on Kubernetes (KubeRay), using spot capacity where it fits; distributed checkpointing; Optuna or Ray Tune | A candidate model artifact |
| 3. Registry and governance gateway | MLflow on a private Postgres backend; DVC dataset hash, Git SHA, and weights linked per artifact; separate first-line and second-line permissions | A promotion approval |
| 4. Serving and runtime | NVIDIA Dynamo-Triton for high-concurrency serving; vLLM for LLM serving; KEDA autoscaling; Argo Rollouts progressive canaries | A production telemetry stream |
| 5. Telemetry and observability | Prometheus and Grafana with NVIDIA DCGM GPU metrics; Kafka event stream; Evidently AI drift tests (Kolmogorov-Smirnov and PSI); circuit breakers that fall back to deterministic rules | Retraining triggers and alerts |
If your roadmap includes large language models, our LLMOps vs MLOps guide explains which of these layers change and which stay the same.
4. The 4-Gate Promotion Gauntlet: Model Risk Control in CI/CD-CT
Gating Rule of Thumb: A risk policy that is not automated is a policy that lapses. If your model governance lives only in a PDF, your production models are exposed. Code the policy into the deployment gates.
Automation turns validation from a manual brake into an engineering accelerator.
We replace sign-off spreadsheets with a four-gate promotion pipeline. A retrained candidate must clear every gate before it receives production traffic. The thresholds below are illustrative, and your validation team should set and document its own for each use case.
The 4-Gate Automated Candidate Promotion Gauntlet
| Gate | Checks | Illustrative threshold |
|---|---|---|
| 1. Statistical superiority and calibration | F1 and ROC-AUC beat the production champion, and a paired McNemar test confirms that the gain is significant. | Chi-square at or above 3.84 (p below 0.05, one degree of freedom) |
| 2. Fairness assertions | Disparate Impact Ratio, Demographic Parity Difference, and Equal Opportunity Difference across the cohorts your policy names. | DIR 0.80 to 1.25; parity and opportunity differences at or below 0.05 |
| 3. Serving SLA and stress benchmark | Ephemeral load test on the serving runtime, bounded GPU memory, and noise-perturbation robustness. | p99 latency under 35 ms at 1,000 requests per second; accuracy drop under 3% under corruption |
| 4. Lineage and data boundary | Dataset hash and Git commit recorded in the registry, production-data residency verified, and a test report archived. | All records present; no production personal data in non-production environments |
| Promotion | Canary rollout with Argo Rollouts. | 2%, 10%, 50%, then 100% of traffic |
McNemar chi-square = ( |b − c| − 1 )² / ( b + c ) >= 3.84
Two cautions on the numbers. The four-fifths rule behind the 0.80 to 1.25 band is a convention from US employment practice, not a MAS requirement, and the Veritas methodologies provide assessment methods but no numeric thresholds. The Population Stability Index levels teams quote for drift (0.10 as a warning and 0.25 as a significant shift) are credit scorecard rules of thumb, so calibrate them to each feature and model.
5. MLOps Engineering Pod Sizing: Team Topologies and 36-Month Cost
Staffing Rule of Thumb: Never hire one MLOps engineer and expect them to run pipelines, Kubernetes, serving, and monitoring alone. Staff a cohesive pod with shared context, so a single resignation does not stall your platform.
Cohesive pods remove single points of failure.
An MLOps engineering pod is a dedicated, cross-functional squad that works as an extension of your engineering department. It follows the platform team pattern from Matthew Skelton and Manuel Pais’s Team Topologies: the pod builds a self-service platform that your data science teams consume, and it measures itself by how little those teams have to ask for. Size the pod to your model portfolio:
MLOps Engineering Pod Tiers
| Pod tier | Team | Active models | Typical fit |
|---|---|---|---|
| Tier 1: Core delivery pod | 4 engineers: lead architect, feature store engineer, serving engineer, telemetry lead | 1 to 5 production models | Scale-ups launching their first continuous training pipelines on MLflow. |
| Tier 2: Enterprise platform squad | 6 engineers: lead architect, two platform or ML engineers, feature store engineer, serving engineer, validation tooling engineer | 5 to 15 production models | Multi-product companies on private Kubernetes clusters and Feast, with regulatory evidence requirements. |
| Tier 3: Regulated AI platform mesh | 8 engineers: principal architect, three platform SREs, two data infrastructure engineers, serving FinOps engineer, validation tooling engineer | 15+ models, or foundation model serving | Financial institutions with high-frequency risk engines, private clouds, and full three-lines-of-defense separation. |
A note on independence: the validation tooling engineer builds the test harness and evidence pipeline. Independent validation sign-off stays with your second line of defense, because a vendor pod that builds a model cannot also be the party that approves it.
36-Month Ledger: Dedicated Pod vs In-House Team vs Project Agency
Running an in-house MLOps team in a high-cost market such as Singapore or Sydney adds costs that do not appear in a salary line: statutory retirement contributions (12% Superannuation in Australia, CPF in Singapore), payroll tax, recruiter placement fees, workstations, desks, and the replacement of engineers who leave. Our In-House vs Outsourced MLOps guide lists every line. In summary, for a four-engineer team over 36 months:
36-Month MLOps TCO, Modeled (USD)
| Delivery model | What the model includes | 36-month total |
|---|---|---|
| A. In-house team (Sydney and Singapore blend) | Salaries, retirement contributions, payroll tax, recruiter fees, hardware, desks, and attrition replacement | $2,378,224 |
| B. Project agency | Initial build SOW, change orders, and ongoing retainers | $1,485,000 |
| C. Dedicated Vinova pod | Flat monthly retainer for four engineers (modeled at $19,500 a month, indexed 4% a year) plus one-time setup | $742,454 |
In this scenario the pod saves $1,635,770 against the in-house team, a 68.8% reduction, and $742,546 against the project agency, a 50.0% reduction. Read the figures as a model, not a quote. Salaries average $145,000 per engineer, exchange rates are set at 1 AUD = 0.655 USD and 1 SGD = 0.765 USD, and payroll tax, the recruiter fee, and the pod price are assumptions. The ledger leaves out cloud compute and your own technical leads’ review time, both of which you will pay in every option.
What Is Inside the Retainer
- Predictable monthly cost: A flat retainer covers the engineers, ISO 27001:2022-certified delivery environments, managed workstations, and local employment compliance.
- Changes inside the retainer: Your engineering leaders reprioritize the backlog in Jira or Linear, and sprints pivot without a contract renegotiation. Work outside the pod’s remit is scoped separately.
- No placement fees: Vinova carries recruitment, vetting, onboarding, and training costs.
- Backfill commitment: If an engineer leaves, Vinova sources, screens, and shadows a qualified replacement within 30 calendar days at no placement cost.
6. Real-World Practitioner Texture
Friction Point We Hit: The Silent Desynchronization in Multi-Corridor Credit Scoring
An institutional digital financing platform processing cross-border SME credit applications across Southeast Asia trained its risk models in Python with local Pandas transformations. When backend engineers reimplemented the real-time scoring service in Go to meet a 25 ms latency target, subtle differences in rolling 30-day timestamp boundaries made the live model evaluate features differently from the training set.
Offline validation AUC of 0.93 decayed to 0.71 in production, and default rates surged by 18%. The infrastructure dashboards in Datadog showed green health, and every endpoint returned a clean HTTP 200.
Vinova resolved this by moving feature definitions into Feast. The same versioned definitions served point-in-time-correct joins from offline Snowflake tables for training and lookups from an online Redis cluster for inference. We paired that with Kolmogorov-Smirnov and PSI drift triggers in Evidently AI, using thresholds the client’s validators set, which restored production AUC to 0.92 and automated the retraining cycle.
| Metric | Before | After |
|---|---|---|
| Production AUC | 0.71, against 0.93 in offline validation | 0.92 |
| Feature logic | Pandas in training, reimplemented in Go for serving | One versioned definition in Feast |
| Monitoring | Infrastructure dashboards only | Drift triggers on the prediction and feature streams |
| Retraining | Manual | Automated on drift triggers |
Friction Point We Hit: The CUDA Out-of-Memory Cascade in Multi-Tenant LLM Document Processing
An enterprise legal and compliance platform served document-processing LLMs from standard Python FastAPI services on AWS g5.12xlarge instances (NVIDIA A10G GPUs). Every morning, 40 analysts submitted multi-thousand-word contracts at the same time.
The naive serving runtime pre-allocated contiguous memory for the maximum context length of 8,192 tokens, and internal fragmentation consumed 78% of GPU memory. The allocator failed with fatal CUDA out-of-memory errors, container pods crashed, and 14% of client requests were dropped during 4-minute restart loops.
Vinova resolved this by moving serving to vLLM with PagedAttention and Automatic Prefix Caching. PagedAttention stored the attention key-value cache in non-contiguous blocks, and prefix caching kept the shared 3,200-token system prompt in GPU memory so recurring queries skipped prefill computation.
| Metric | Before | After |
|---|---|---|
| GPU memory waste | 78% lost to fragmentation | Under 4% |
| Time to first token | 2.8 seconds | 190 milliseconds |
| Throughput on identical GPUs | Baseline | 3.4 times the baseline |
| Out-of-memory restarts | 14% of requests dropped during peaks | Resolved |
7. Real-World Client Field Proof
Vinova Field Insight: Automating Model Governance for an Institutional Payment Engine
A Singapore-headquartered Major Payment Institution licensed under the Payment Services Act 2019 ran transaction risk scoring and real-time merchant settlement models that processed over 12 million inferences a day.
The Compliance Bottleneck: After expanding into cross-border trade financing corridors, the models suffered silent concept drift. Offline validation AUC of 0.92 had decayed to 0.72 in production. The compliance committee halted all model updates because manual validation reviews took 14 weeks, and internal audit warned that the manual process could not evidence continuous validation.
The Vinova Systems Solution: (1) Dedicated governance pod: a six-person engineering pod (one lead AI architect, two MLOps platform engineers, one feature store specialist, one QA telemetry lead, and one validation engineer) working across Singapore and Vietnam under a Singapore Master Services Agreement. (2) Four-gate promotion pipeline: an automated CI/CD-CT verification pipeline replaced the validation spreadsheets, with fairness gates asserting a Disparate Impact Ratio between 0.85 and 1.15 across corridor cohorts. (3) Production-data boundary: private VPC subnets in AWS Singapore (ap-southeast-1) kept regulated transaction records in the client’s perimeter, while the pod worked on deterministically masked synthetic testbeds. (4) Telemetry and circuit breakers: Evidently AI consumed a Kafka stream, and a PSI of 0.20 or higher launched containerized retraining DAGs on Kubernetes, with automatic fallback to deterministic business heuristics if latency breached 35 ms.
The Measurable Impact: The full results are in the table below.
| Metric | Before | After |
|---|---|---|
| Validation cycle | 14 weeks of manual review | Retraining, independent validation, and canary promotion in under 45 minutes |
| Production ROC-AUC | 0.72 after silent drift | 0.91, maintained |
| Fraud losses | Unflagged fraudulent chargebacks | An estimated S$680,000 in chargebacks prevented over the first two quarters |
| Auditability | Manual spreadsheet lineage | Every live transaction decision traceable to its model hash and training run ID; no lineage findings raised in the subsequent supervisory review |
Explore Vinova’s AI & Machine Learning Services
See how automated promotion gates, segregated model registries, and drift telemetry fit your own platform, and book an enterprise MLOps architecture assessment.
8. Contract and IP Governance: How a Vinova Engagement Is Structured
Fiduciary Rule of Thumb: Settle ownership and dispute resolution in the contract before the first commit. Both are far easier to fix on paper than in a dispute.
Cross-border engineering needs a contract your general counsel can defend.
Where your engineering capacity sits changes your legal exposure, so we anchor every engagement in Singapore. Four governance gates apply to each one:
The Four Governance Gates of a Vinova Engagement
| Gate | How it works | What your counsel should check |
|---|---|---|
| 1. Direct Singapore MSA | You contract directly with Vinova Pte. Ltd., a Singapore company, under a Master Services Agreement governed by Singapore law. | Governing law, liability caps, and data protection terms. |
| 2. IP assignment | The agreement assigns ownership of the deliverables, including future copyright, to you. Section 140 of the Singapore Copyright Act 2021 provides for the assignment of future copyright, so ownership can vest in you when the work is created. | The assignment wording, treatment of open-source components, and pre-existing tools. Have Singapore counsel confirm the drafting. |
| 3. SIAC arbitration | Disputes go to binding arbitration administered by the Singapore International Arbitration Centre under its 2025 Rules. Awards are enforceable in the 172 Contracting States of the 1958 New York Convention, subject to the Convention’s grounds for refusing enforcement. | Number of arbitrators, language, and any carve-outs for injunctive relief. |
| 4. Production-data boundary | Regulated customer data stays in your cloud perimeter. Pods work on masked or synthetic test data. | Your cross-border transfer obligations (in Singapore, the PDPA Transfer Limitation Obligation in section 26) and whether masked data still counts as personal data. |
Agreement signed + assignment of future copyright + work created in a commit => ownership vests in the client
Your code stays in your own GitHub or GitLab repositories under your IAM controls, and your onshore technical leads hold merge approval and veto over the main and production branches. The engineers who write it are employed in Vinova’s Vietnam delivery hubs under written terms that include confidentiality and IP provisions, and Vietnam’s Labour Code allows employers and employees to agree in writing on the protection of trade secrets and technology know-how. Your counsel can review those terms during diligence.
9. 60-Day MLOps Platform Roadmap: From Assessment to Production Canary
A stage-gated roadmap de-risks the platform build. Each phase ends with something running that you can inspect:
The 60-Day MLOps Platform Deployment Roadmap
| Weeks | Phase | What happens |
|---|---|---|
| 1 to 2 | Discovery and calibration | Audit data sources, training scripts, notebook dependencies, and serving endpoints. Profile GPU memory and set baseline latency and cost targets. Calibrate the gate matrix to the regimes you report under, and to ISO/IEC 42001 if you pursue it. Finalize the pod and sign the MSA. |
| 3 to 4 | Pod seating and data foundation | Seat named senior engineers. Grant repository access through zero-trust network access with multi-factor authentication. Deploy Feast definitions across the offline and online stores, and add Great Expectations validation at ingestion boundaries. |
| 5 to 6 | Pipeline automation and serving | Containerize training into Argo Workflows. Deploy NVIDIA Dynamo-Triton or vLLM with dynamic batching. Codify the four gates in GitLab CI or GitHub Actions, and mirror shadow traffic to candidates to validate latency and accuracy baselines. |
| 7 to 8+ | Canary and operations | Launch progressive canaries (2%, 10%, 50%, 100%) with Argo Rollouts. Activate sliding-window drift telemetry from Kafka. Route Prometheus alerts to PagerDuty with SEV-1 to SEV-4 severities, and move to continuous sprint delivery under joint product owner governance. |
Sixty days is a typical path for a Tier 1 pod with a defined model portfolio and prompt access to systems. Data quality, security reviews, and your own review turnaround will move it.
10. Frequently Asked Questions (FAQ)
What is the difference between hiring an MLOps consultant and a dedicated MLOps engineering pod?
Traditional consultancies, including management and Big Four advisory firms, supply senior strategists who assess processes and deliver assessment reports. That is valuable for strategy, but many of them do not write production code, maintain Kubernetes clusters, or take the pager when CUDA out-of-memory errors appear at 2 AM.
Vinova deploys dedicated, full-stack MLOps engineering pods (lead architects, feature store engineers, serving specialists, and telemetry leads) who work inside your Git repositories, build your CI/CD-CT pipelines, tune your serving runtimes, and stay on to operate them under a predictable monthly retainer.
What does an MLOps architecture assessment involve, and how long does it take?
An MLOps architecture assessment takes 2 to 4 weeks. We audit your data sources, pipelines, serving endpoints, and monitoring, score you on a 20-point scorecard, profile GPU utilization and cloud spend, and deliver an audit report with a stage-gated 90-day roadmap. You can act on the roadmap with your own team or with a Vinova pod.
How much do MLOps consulting services cost?
The assessment is scoped as a 2 to 4 week engagement, and pods are priced as a flat monthly retainer by tier. In the 36-month scenario modeled above, a four-engineer pod costs about $742,000, against about $2.38M for an in-house team and about $1.49M for a project agency. Your own numbers will differ with salaries, location, and scope, so we price after the assessment, against your model portfolio.
How does Vinova protect our intellectual property and proprietary algorithms?
Protection comes from contract, repository control, and data boundaries working together:
- Singapore jurisdiction: You contract directly with Vinova Pte. Ltd. under Singapore law, with SIAC arbitration for disputes.
- IP assignment: The agreement assigns ownership of code and models to you, including future copyright under Section 140 of the Singapore Copyright Act 2021. Your counsel should confirm the wording.
- Repository sovereignty: All code, DAGs, and Dockerfiles live in your own repositories under your IAM controls.
- Merge veto: Our engineers cannot merge to main or production branches. Your onshore leads hold final approval.
What should model risk management services Singapore institutions expect from an MLOps partner, and how do they align with the MAS Guidelines and FEAT?
Expect a partner that builds platforms generating the evidence the MAS AI Risk Management Guidelines and the FEAT Principles ask for. We do not certify compliance for you, and no vendor can, because the obligation sits with your institution.
- Inventory, materiality, and validation: The model registry holds the inventory and the risk rating, and enforces independent second-line approval before promotion.
- Fairness testing: We integrate Veritas-style assessment methods into CI/CD-CT, using the metrics and thresholds your validation team documents.
- Lineage and accountability: Every production prediction carries its model version hash, pipeline run ID, dataset snapshot hash (through DVC), and Git commit SHA.
- Monitoring: Drift monitors trigger retraining or fallback, so continuous validation is a running control and not a quarterly review.
Can Vinova work with our existing cloud infrastructure on AWS, GCP, or Azure?
Yes. We deploy modular open-source toolstacks (Feast, MLflow, Argo Workflows, NVIDIA Dynamo-Triton, vLLM, and Evidently AI) on your existing environment, whether that is Amazon EKS, Google GKE, Azure AKS, or an on-premise Kubernetes cluster. We do not require a proprietary third-party control plane, and where you already run managed services, we integrate with them rather than replace them.
MLOps Consulting Services, Resolved: Build Production AI You Can Defend with Vinova
Production machine learning should create commercial leverage. It should not trap your organization in stalled hiring cycles, runaway cloud costs, or silent model decay.
For 16+ years, Vinova has partnered with technology scale-ups, multinational enterprises, and government statutory bodies across Singapore, Australia, and the US to build scalable digital platforms, secure cloud architectures, and production MLOps systems:
- Fiduciary Governance & Regulatory Alignment: Delivery operations certified under ISO/IEC 27001:2022 (Information Security) and ISO 9001:2015 (Quality Management) and GovTech Category 1B approved, with architectures designed to align with the MAS AI Risk Management Guidelines, the FEAT Principles, and the model risk expectations of the regulators you report to.
- Singapore Corporate Governance: Master Services Agreements governed under Singapore law with SIAC arbitration, so intellectual property ownership and accountability are defined before the first commit.
- Enterprise AI, MLOps & Model Governance Practice: Dedicated pods specializing in high-throughput inference serving (vLLM, NVIDIA Dynamo-Triton), feature stores (Feast), containerized CI/CD-CT pipelines (Argo, KubeRay), and drift observability (Evidently AI).
- Engineering Depth: 300+ in-house engineers across Singapore and Vietnam delivery hubs in Ho Chi Minh City, Da Nang, and Hanoi, with 8% to 12% annual voluntary attrition, so the people who build your platform are still there to run it.
Ready to audit your MLOps architecture and put governance in the pipeline? Book an enterprise MLOps architecture assessment, benchmark your platform with our MLOps maturity model, or schedule an AI systems architecture consultation with our technical directors today.
Vinova: a Singaporean Government-Grade Digital Transformation Partner, Made Accessible. For 16 years, we have designed digital systems for 300+ companies and government agencies worldwide, backed by ISO 27001:2022 and ISO 9001:2015 certification and Singapore GovTech Category 1B approval.
300+ employees across offices in Singapore, Vietnam (Hanoi, Da Nang, Ho Chi Minh City), Norway (Oslo), and Thailand (Bangkok), scaling platforms and engineering capacity to serve clients across the globe.
Financial Times Top 500 High-Growth Companies Asia-Pacific 2026. Recognized among Singapore’s Top 100 Fastest-Growing Companies in 2024, 2025, and 2026.