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Machine Learning & MLOps Services in the US

Turn experimental models into reliable, revenue-driving ML products. Stralya designs, builds and operates robust MLOps foundations so your machine learning runs safely at scale in the cloud and integrates cleanly with your web and ecommerce stack.
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Service scope

What Our Machine Learning & MLOps Service Includes

Our Machine Learning & MLOps offering is designed for organisations that treat ML as a strategic asset, not an experiment. We cover the full lifecycle – from data and architecture to deployment and long-term operations – with a clear, structured scope tailored to US-based B2B digital products, web applications and ecommerce platforms.

Core components of the ML & MLOps service

Assessment of your current ML stack, data landscape, tools and team capabilities, and how they connect to your web and ecommerce ecosystem.
Design of cloud-native ML and MLOps architecture on AWS, Azure or GCP, aligned with your security, compliance and performance requirements.
Implementation of reproducible environments using containers and infrastructure as code, consistent with your broader web development practices.
Creation or hardening of data pipelines, feature stores and model registries to support reliable training and deployment.
Setup of CI/CD pipelines for ML, including automated testing and quality gates, to reduce deployment risk and lead time.
Design and deployment of real-time APIs, batch jobs or streaming ML workloads that integrate with your applications and digital products.
Implementation of monitoring, logging, alerting and model performance dashboards, giving you full observability of ML in production.
Drift detection, retraining workflows and governance for model lifecycle management across all critical ML use cases.
Drift detection, retraining workflows and governance for model lifecycle management across all critical ML use cases.
Drift detection, retraining workflows and governance for model lifecycle management across all critical ML use cases.

Optional add-ons and extensions

Extended governance, compliance and auditability for regulated industries and sensitive ML workloads.
Advanced experimentation platforms (feature flags, A/B testing, online evaluation) to safely roll out new ML-driven features.
Advanced experimentation platforms (feature flags, A/B testing, online evaluation) integrated with your web and ecommerce stack.
Custom analytics and reporting dashboards for business stakeholders, tying ML performance to revenue, cost and CX KPIs.
Staff augmentation of senior cloud, data and MLOps engineers to reinforce your internal team on strategic projects.
24/7 or extended-hours production support for critical ML services that underpin key web or ecommerce journeys.
Integration with existing corporate data platforms and BI tools to ensure a single source of truth for analytics and reporting.
Security reviews and hardening in collaboration with your InfoSec team, aligned with your organisation’s standards and policies.
Every engagement starts with a structured discovery and scoping phase. From there, we build a tailored, fixed-price roadmap that matches your priorities, risk appetite and internal capabilities – ensuring your machine learning becomes a dependable part of your digital infrastructure, not a fragile side project running in isolation from your main web and ecommerce platforms.

Outcomes You Can Expect from Our ML & MLOps Work

From experiments to measurable impact
Your ML initiatives move from isolated notebooks to production systems that directly support revenue, efficiency or customer experience – with clear KPIs and reporting tied to your digital products and ecommerce channels.
Performance, scalability and cost control
Cloud-native architectures ensure your models scale with demand while keeping infrastructure and inference costs under control through right-sized, observable workloads that align with how you operate your web platforms.
Reduced operational risk
Monitoring, alerting, drift detection and clear incident processes dramatically reduce the risk of silent model failures, degraded user experiences or compliance issues, even during peak traffic or campaign periods.
Empowered internal teams
Your data scientists, engineers and product teams gain a shared, well-documented platform and workflow, enabling them to collaborate efficiently and ship ML features faster across your applications and sites.
Strategic advantage in your market
With reliable ML capabilities embedded into your web products and ecommerce experiences, you differentiate in a competitive US B2B landscape through smarter, more personalised and more efficient digital journeys.

How we work

A Structured MLOps Process from Idea to Stable Production

Every organisation is at a different stage in its ML journey. Some have models living in notebooks, others depend on legacy pipelines that are hard to support. Our approach adapts to your maturity, but always follows a rigorous, cloud-native process designed to reduce risk, increase predictability, and fit alongside your existing web development and ecommerce platforms.

We start by building a clear picture of your current ML initiatives, data sources, infrastructure and business objectives. We review existing models, pipelines, tools and team workflows, then map gaps against best-practice MLOps standards for reliability, security and scalability, including how ML connects to your customer-facing web and ecommerce experiences.
We design a target MLOps architecture tailored to your cloud (AWS, Azure or GCP), data stack and security constraints. You receive a clear, fixed-price scope covering environments, CI/CD, model serving, monitoring and governance – with realistic timelines and explicit responsibilities – so your ML work can support core platforms such as your website and ecommerce storefront.
We build the core building blocks: infrastructure as code, containerisation, ML pipelines, feature stores, model registries, automated testing, and deployment workflows. Everything is versioned, reproducible and aligned with your existing engineering practices, whether you run custom web apps, Shopify web development services, or other digital channels.
We take your models from notebooks to production endpoints or batch jobs, with strong observability: performance dashboards, alerts, drift detection and logging. We collaborate with your team to tune performance, cost and reliability, and to define SLAs that match the criticality of each use case, from internal analytics to customer-facing personalization on your website.
We document everything and upskill your team so they can confidently operate and extend the platform. Stralya can then stay on as a long-term partner – maintaining, improving and scaling your ML capabilities as your business, traffic and data grow across your broader digital and ecommerce ecosystem.

Popular Questions

Find Commonly Asked Questions

MLOps (Machine Learning Operations) is the set of practices, tools and processes that turn ML models into reliable, maintainable production systems. For US organisations running high-visibility digital products and ecommerce sites, MLOps is critical to avoid fragile, manual deployments that break under real-world usage. It ensures your models are versioned, tested, deployed, monitored and retrained in a controlled, auditable way – aligned with your cloud, security and compliance standards, and with your broader web development practices.
Yes. Many of our engagements are with teams that have strong data science capabilities but lack the engineering capacity to industrialise their work. Stralya focuses on the MLOps and cloud-native engineering layer: infrastructure, pipelines, deployment, observability and governance. Your data scientists keep focusing on modelling, while we ensure their models can run safely and efficiently in production and power real features across your web and ecommerce properties.
We are cloud-native and work with all three major cloud providers: AWS, Azure and GCP. We leverage their managed ML and data services when they make sense, and combine them with containers, Kubernetes or serverless architectures depending on your requirements, cost constraints and existing stack – the same principles we apply when supporting large-scale web development or ecommerce platforms.
Yes. Stralya is often asked to rescue ML initiatives that are stuck in proof-of-concept mode, or that fail frequently in production. We start with a structured audit of your code, pipelines, infrastructure and processes, then propose a stabilisation and remediation plan. Our goal is not only to fix what is broken, but to leave you with a robust MLOps foundation that prevents the same issues from reappearing and supports your long-term digital roadmap.
Our primary model is fixed-price, project-based. After an initial discovery and scoping phase, we define a clear scope, deliverables, timelines and responsibilities, then commit to a fixed budget. For selected organisations, we can also provide senior MLOps and cloud engineers in staff augmentation mode, usually to reinforce an existing team on a strategic initiative linked to your core web or ecommerce platforms.
Absolutely. Stralya is first and foremost a cloud-native web development company. We design ML APIs, batch jobs and streaming pipelines that integrate cleanly with your existing web platforms, microservices and data infrastructure. This ensures your ML features feel like a natural extension of your product, not a separate, fragile add-on – whether you run custom applications, enterprise portals or Shopify web development services for ecommerce.

Case Studies

Real solutions Real impact.

These aren’t just polished visuals they’re real projects solving real problems. Each case study 
apply strategy, design, and development.

View Work

Building a Monolithic Headless CMS and Frontend with Next.js

A monolithic headless CMS, engineered with React and Next.js App Router to power high-performance websites, Shopify web development services, and product frontends fast, with clean content operations for non-technical teams.

6

weeks from first commit to a production-ready CMS core.

3x

faster time-to-market for new marketing and product pages.

View Project Details
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Mandarin Learning Platform Project Takeover and Recovery

Taking over a third-party Mandarin e-learning platform to secure, stabilize and restructure critical cloud-native components for long-term growth.

6

weeks to stabilize and secure the core platform after takeover.

0

critical incidents in production after Stralya’s recovery phase.

View Project Details

Client Testimonials

What Our Clients Say

Professional, fast, and ultra-responsive. A company with a top-notch mindset.

Omar Ouhmad

CEO, Growth Hackerz

A company with a true entrepreneurial spirit: fast, professional, and above all, responsive.

Laurent Phillipe

CEO, MandarinMaster

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