Production ML and AI Platform Engineering

MLOps and AI Platform Development Services
Turn Models, Prompts, and Pipelines Into Reliable Production Systems

Devlyn builds MLOps and AI platforms for teams that need reproducible training, experiment tracking, model registries, feature stores, evaluation gates, CI/CD, model serving, prompt and model versioning, drift monitoring, governance workflows, cost telemetry, and production handover. We help data science, ML engineering, platform, and product teams move from notebooks and manual releases to auditable, observable, repeatable AI delivery.

Assess our MLOps platform See platform scope

Model lifecycle

Registry, lineage, approval

ML CI/CD

Train, eval, deploy

Production monitoring

Drift, quality, cost

Models reach production safely only when the lifecycle is engineered

ML and AI releases involve code, data, features, model weights, prompts, evaluation datasets, infrastructure, approvals, and monitoring. If those artifacts are not versioned and governed together, every release becomes hard to reproduce, hard to audit, and hard to roll back.

What breaks

Data scientists train useful models, but deployment depends on ad hoc scripts, manual handoffs, unclear environments, and undocumented assumptions.

There is no shared registry linking model versions to code, datasets, features, metrics, prompts, approvals, owner, deployment target, and rollback path.

Evaluation happens before release, but production drift, quality regression, feature distribution change, or prompt behavior change is not tracked with operational owners.

Serving infrastructure is designed for one model at a time, then breaks when teams need canary rollout, batch inference, online endpoints, GPU scheduling, or multiple model families.

Governance and security reviews happen late because lineage, access, artifact signing, PII handling, approval records, and evidence capture were not part of the platform.

How Devlyn reduces risk

We design an MLOps platform architecture around your maturity level: experiment tracking, model registry, feature store, CI/CD, serving, monitoring, governance, and team workflow.

We automate the path from training to evaluation to approval to deployment so models, prompts, and data-dependent artifacts move through visible gates.

We integrate serving and rollout patterns such as batch jobs, online APIs, canary releases, shadow traffic, rollback, GPU inference, and cost-aware deployment.

We connect monitoring to ownership: input drift, feature drift, prediction drift, quality metrics, business KPIs, latency, cost, incidents, and retraining triggers.

We hand over runbooks, platform documentation, IaC, templates, dashboards, release process, and onboarding guides so the platform survives team changes.

What we deliver in MLOps and AI platform development

The service creates the production path for ML and AI teams. The stack can be cloud-managed, Kubernetes-native, open-source, warehouse-centered, or hybrid depending on your requirements.

01

Platform architecture and maturity assessment

Review current notebooks, pipelines, deployments, cloud resources, data workflows, ownership, release process, monitoring, and governance gaps.

02

Experiment tracking and model registry

Implement or improve model registry, experiment tracking, lineage, metadata, tags, approvals, environments, version aliases, and artifact storage.

03

Training and evaluation pipelines

Build reproducible training, validation, evaluation, prompt testing, data checks, feature validation, and quality gates using orchestrators and CI/CD.

04

Feature store and data pipeline integration

Design feature definitions, offline and online stores, point-in-time correctness, data contracts, batch and streaming inputs, and lineage into model runs.

05

Model serving and deployment automation

Deploy models through online endpoints, batch inference, streaming inference, KServe, BentoML, SageMaker, Vertex, Triton, vLLM, or custom services.

06

Monitoring, governance, and operations handover

Create drift monitoring, quality dashboards, cost telemetry, incident paths, retraining triggers, runbooks, platform docs, and team onboarding.

The platform layers we build or improve

A useful MLOps platform is a set of paved paths. It should reduce friction for data scientists while giving engineering, security, and operations enough control for production.

Tracking and metadata layer

Track experiments, parameters, metrics, artifacts, prompts, datasets, code versions, environment details, lineage, approvals, and deployment targets.

Data and feature layer

Connect data validation, feature definitions, offline and online feature stores, training datasets, prediction inputs, and point-in-time joins.

Pipeline orchestration layer

Use Airflow, Dagster, Prefect, Argo, Kubeflow Pipelines, GitHub Actions, or cloud workflows for training, evaluation, retraining, and batch inference.

Serving and rollout layer

Support online inference, batch inference, GPU serving, autoscaling, canaries, shadow deployments, rollback, model routing, and API contracts.

Monitoring and alerting layer

Monitor data drift, feature drift, prediction distribution, model quality, business KPIs, latency, errors, cost, and retraining triggers.

Governance and security layer

Add approvals, access controls, environment separation, artifact retention, documentation, policy checks, evidence packs, and audit support.

Tooling choices we can work with

We do not force one MLOps stack. We select and integrate tools based on cloud strategy, model volume, latency, compliance, team skills, and operational burden.

Use tracking and registries to connect runs

models

parameters

metrics

artifacts

prompts

datasets

approvals

deployment records

Use orchestration for training

evaluation

evaluation

feature computation

retraining

batch inference

data validation

scheduled workflows

Use feature stores when teams need reusable feature definitions

online/offline consistency

point-in-time correctness

model-lineage clarity

Use serving layers for online inference

GPU serving

canaries

scaling

endpoint contracts

latency control

model rollout

Use managed platforms when they fit your cloud

security

governance

data location

operational maturity requirements

Use IaC and policy gates to make environments reproducible

reviewable

secure

aligned with platform operations

MLOps and AI platform engagement models

Scoped options for teams standardizing the model lifecycle and production AI operations.

Assessment

MLOps Maturity Assessment

Best when model delivery is manual or inconsistent

Scoped

after discovery

Current-state audit

Target architecture

Gap map

Platform roadmap

Talk to Sales
Most Popular

Build

Production MLOps Platform Path

Best for turning one critical model lifecycle into a reusable platform path

Scoped

after discovery

Tracking and registry

Eval and deploy gates

Serving automation

Monitoring handover

Talk to Sales

Scale

AI Platform Expansion Support

Best for multi-team platform adoption and governance

Scoped

after discovery

Templates and SDKs

New model patterns

Governance workflows

Platform roadmap

Talk to Sales

Who this service is for

MLOps platform work is most valuable when the organization has real models or AI services, but the delivery path is still too manual, fragile, or ungoverned.

01

Data science teams entering production

You have models that work in notebooks but need deployment, evaluation, monitoring, registry, and ownership to become products.

02

Platform teams standardizing ML delivery

You need a reusable path for training, approvals, serving, monitoring, and governance across teams instead of each team inventing its own stack.

03

Regulated or audit-sensitive model programs

You need lineage, approvals, model cards, evidence, rollback, data controls, access rules, and monitored behavior for production models.

04

GenAI and LLMOps teams

You need prompt/model versioning, evaluation gates, observability, cost telemetry, deployment controls, and quality monitoring for LLM-powered systems.

Security, governance, and operating handover

A platform is only useful if teams can trust it. We make ownership, controls, and operational responsibility explicit.

01

Artifact and environment controls

Define artifact storage, image scanning, secrets handling, environment separation, access control, deployment policies, and signed or approved releases.

02

Lineage and evidence

Link code, data, features, model versions, prompt versions, metrics, approvals, deployment targets, monitoring events, and incident records.

03

Operational ownership

Assign owners for model releases, platform changes, monitoring alerts, retraining decisions, cost review, incident response, and user support.

04

No platform shelfware

We build around real team workflows, templates, examples, onboarding, and the first production path so adoption is practical rather than aspirational.

Build the production path your models can keep using

Share your model lifecycle, current tools, deployment pain, and platform goals. We will help you identify the smallest MLOps platform path that removes the most release risk.

Assess our MLOps platform View AI observability service

hello@devlyn.ai

Model registry

ML CI/CD

Serving automation

Monitoring handover

Frequently Asked Questions

Direct answers for teams comparing MLOps platform development, model registry implementation, ML CI/CD, model serving, drift monitoring, and AI platform engineering services.

They include platform assessment, experiment tracking, model registry, training pipelines, evaluation gates, feature store design, model serving, CI/CD, monitoring, governance workflows, documentation, and handover.

MLOps includes DevOps practices but also manages data, features, model artifacts, training runs, evaluation metrics, drift, retraining, approvals, lineage, and model-specific monitoring.

Yes. We can implement or improve a registry using MLflow, managed cloud registries, Databricks, SageMaker, Vertex AI, Azure ML, or a custom metadata layer depending on your stack.

Yes. We can build pipelines for data validation, training, evaluation, model registration, approval, deployment, rollback, and monitoring using CI/CD and workflow orchestration tools.

Not always. A feature store helps when teams need reusable features, online/offline consistency, point-in-time correctness, and shared feature governance across multiple models.

Yes. We can use KServe, BentoML, Seldon, Triton, vLLM, custom services, or cloud-managed endpoints depending on latency, scaling, model type, and team operations.

Yes. We can work with SageMaker, Vertex AI, Azure ML, Databricks, Snowflake, and other managed platforms when they fit your cloud and governance strategy.

Yes. We can extend platform patterns to prompt versioning, evaluation datasets, LLM observability, model routing, cost telemetry, and release gates for GenAI systems.

Monitoring can include feature distribution changes, input drift, prediction drift, quality metrics, business KPIs, latency, error rates, cost, and user feedback where available.

Yes, but retraining should follow data quality, evaluation, approval, and release controls. We design retraining triggers and gates based on the model risk and operational context.

Governance can include lineage, approval workflows, access control, model cards, evidence packs, risk registers, environment separation, retention rules, and audit-friendly documentation.

Useful inputs include current repositories, model examples, data pipelines, deployment scripts, cloud accounts, security constraints, monitoring gaps, team workflow, and production goals.

Yes. We prefer to reuse viable tools and workflows before adding new platform components. The platform should reduce operational burden, not add a parallel stack.

Handover can include IaC, pipeline templates, registry documentation, serving patterns, dashboards, runbooks, access model, team onboarding, and a platform roadmap.

How the MLOps platform engagement runs

We start with the path from model idea to production decision. Then we build the smallest platform layer that removes real delivery risk.

We review model development, data pipelines, code repositories, experiments, deployment scripts, cloud resources, monitoring, governance, and team handoffs.
Audit current lifecycle
We agree on platform goals, users, model types, deployment patterns, security rules, approval gates, service levels, and operational ownership.
Define platform target state
We implement the first production path across tracking, registry, evaluation, serving, monitoring, and deployment automation for a representative model or AI workflow.
Build the critical path
We add tests, data validation, lineage, drift checks, approvals, rollback, cost telemetry, access control, and release evidence where needed.
Add reliability and governance gates
We create templates, SDKs, docs, examples, runbooks, onboarding guides, and paved paths so teams can reuse the platform without bespoke help every time.
Onboard users and templates
We hand over dashboards, runbooks, IaC, backlog, review cadence, owner map, and maturity roadmap for future models and AI services.
Handover operations and roadmap