The MLOps consultancy that has operated machine learning in production since 2013. We deliver MLOps consulting, platform implementation (MLflow, Kubeflow, SageMaker, Vertex AI, Seldon), managed services and governance for enterprise AI teams. Multi-cloud, on-prem and air-gapped engagements.
Tell us about your MLOps audit, platform implementation, managed-services need or LLMOps rollout, and we'll tailor an approach. Typically two to four weeks from first call to kick-off.
An MLOps consultancy turns experimental machine learning into reliable production AI. That means deployment pipelines, model monitoring, governance, retraining, observability and the operational guardrails that regulated industries demand. Winder.AI delivers end-to-end MLOps consulting and development as one engagement, audit, platform implementation (MLflow, Kubeflow, SageMaker, Vertex AI, Seldon), MLOps managed services and LLMOps, by the same senior engineers who shipped production AI for Tractable, Interos, NewDay and BlueMotor Finance. No strategy decks without code behind them.
2026 update. Model monitoring is now the single biggest source of MLOps incidents we are called in to fix. The pattern is familiar: a model ships, drift creeps in over weeks, a downstream metric quietly degrades, and nobody finds out until a customer complains or a regulator asks. The fix is rarely a new platform, it is instrumenting drift, data-quality and performance monitoring at deploy time, wiring alerts into the existing on-call rota, and rehearsing the retraining path before you need it. We bake monitoring into every MLOps engagement rather than selling it as a separate phase.
| Provider type | What they deliver | Best for | Main weakness |
|---|---|---|---|
| Big-4 / global IT consultancy | Strategy decks, roadmaps, large delivery teams | Multi-year transformation programmes | Hands-on MLOps engineering offshored or thinly staffed |
| Cloud vendor professional services | Reference implementations on the vendor's stack (SageMaker, Vertex AI, Azure ML) | Adopting a single chosen cloud platform | Lock-in by design, weak on multi-cloud or on-prem |
| Generalist AI agency | Broad AI capability with MLOps as one offering | Bundled vendor relationships | Shallow MLOps bench, weak on governance and regulated environments |
| MLOps platform vendor (with services) | Their platform, plus implementation services around it | Standardising on a single MLOps tool | Conflict of interest, every problem looks like their platform |
| In-house build (your team) | An MLOps platform assembled by your existing ML and platform engineers | Long-term ownership when you already have a senior platform team with spare capacity and production ML experience | Learning curve on monitoring, retraining, lineage and governance delays first production model by 9 to 18 months |
| Specialist MLOps consultancy (Winder.AI) | MLOps audit, platform implementation, managed services and LLMOps, delivered by senior MLOps engineers | Enterprises that need production MLOps, multi-cloud, in regulated industries | Boutique scale, not designed for 100-seat staff augmentation |
Winder.AI is the MLOps consulting partner for organisations that need machine learning to run in production, not in a notebook. Our MLOps consulting services span audit and roadmap, MLOps development services and platform implementation, and ongoing MLOps managed services, the full lifecycle, by senior engineers who have operated ML at scale since 2013.
I would recommend Winder.AI because they are experts with real-world experience, led by Phil Winder, who is well-respected in the industry. They are quick to respond, quick to scale up and they deliver when you need them to.
Twelve years of production MLOps across regulated industries, multi-cloud delivery and a senior engineering bench, not a sales layer.
Production MLOps delivered across finance, insurance, technology, manufacturing and energy.
Production MLOps is the difference between an experimental model and a reliable business system. Winder.AI delivers MLOps solutions as discrete service lines, from audit and platform implementation through to managed services and LLMOps, so you can engage at any point in your MLOps maturity:
We cover the full MLOps stack across the major platforms, MLflow, Kubeflow, SageMaker, Vertex AI and Seldon, and the operational disciplines that turn machine learning into reliable production AI:
Case study
Winder.AI helped Apartment List modernize its machine learning operations by unifying data pipelines, automating Kubeflow workflows, and introducing enterprise-grade governance. The outcome: consistent training and inference data, faster deployment cycles, and self-service capabilities that enabled Apartment List’s data science team to scale model delivery with confidence.
Case study
Interos, a leading supply chain management company, partnered with Winder.AI to enhance their machine learning operations (MLOps). Together, we developed advanced MLOps technologies, including a scalable annotation system, a model deployment suite, AI templates, and a monitoring suite. This collaboration, facilitated by open-source software and Kubernetes deployments, significantly improved Interos’ AI maturity and operational efficiency.
Case study
Tractable.AI is a leading insure-tech company based in the UK and has made significant strides in the motor vehicle insurance sector by leveraging AI technologies. Their innovative approach has allowed them to automate various aspects of the insurance lifecycle, including the complex process of loss adjustment. This AI-driven strategy has not only increased their operational efficiency but also enhanced their service delivery, making them a preferred choice for many customers.
MLOps
Machine learning (ML) model monitoring is a crucial part of the MLOps lifecycle. It ensures that your models are performing as expected and that they are not degrading over time. There are many tools available to help you monitor your models, from open-source frameworks to proprietary SaaS solutions. In this article, I’ll compare some of the best open-source and proprietary machine learning model monitoring tools available today.
MLOps
In an insightful session presented by Enrico Rotundo, we explore the innovative approach to scaling StableAudio globally. This presentation sheds light on the synergy between NVIDIA Triton and AWS SageMaker.
MLOps
Dr. Phil Winder shares experiences of Winder.AI’s MLOps consulting experience at a variety of large and small organizations.
In this talk he presents industry observations of MLOps team size and structure for a range of business sizes and domains. Learn more about how others structure their MLOps teams. Discover which problems you need to solve first.
Welcome to Winder.AI talks. A series of free interactive webinars hosted by Dr Phil Winder, CEO of Winder.AI, Author of O’Reilly’s Reinforcement Learning and one of the founders of the MLOps Community, covering a range of topics about the use of machine learning operations (MLOps), reinforcement learning (RL), and machine learning (ML) in industry today.
This page provides answers to our most common questions. If you have a query that isn't covered, please get in touch.
Whether you need an MLOps audit, a platform implementation on MLflow, Kubeflow, SageMaker or Vertex AI, ongoing managed MLOps services or LLMOps for production language models, talk to the team that has been operating machine learning in production since 2013.
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