Sensational MLOps Services

Tired of fragile ML processes? Our MLOps services deliver resiliency, automation, and unification across your organization.

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What Does MLOps Mean?

MLOps empowers engineers and data scientists to produced production-quality ML models.

What is MLOps?

MLOps is a collection of tools and methodologies that aim to productionize and operationalize machine learning development.

If you ask three data scientists to implement a machine learning (ML) solution you will receive 3 different methodologies, stacks, and operational viabilities. MLOps processes attempt to standardize and unify the development of projects to enhance security, governance, and compliance. MLOps technologies automate repetitive tasks like training a production model or deploying solutions into production.

MLOps is more than just a set of technologies. It doesn’t matter if you’re building your MLOps process on Azure, AWS or GCP. The goals are the same.

MLOps is more than a CI/CD process. It’s a combination of tools and ways of working, ideologies that derive from DevOps, that are unique to each business.

What Is MLOps Not?

MLOps is not a single platform.

There are many products that claim to be MLOps. But productionizing machine learning (ML) and reinforcement leaning (RL) models is much more than being able to serve a model on an endpoint. Running successful, resilient, scalable ML and RL takes time and requires significant expertise.

There are products that suggest that if you combine model training and model serving, you have an MLOps system. But that misses the point of the value of implementing MLOps. The whole point is to improve the quality of service of a model. And this includes items such as auditing and cyber security, which are often neglected by vendors.

In fact, true MLOps involves a whole range of other development tasks that are just as important:

  • Authentication and Authorization
  • Operational maintenance, ownership, and support
  • Disaster recovery
  • Monitoring and alerting
  • Automated testing (both data and model)
  • Auditing
  • Schema management
  • Provenance
  • Scalability (including to zero)
  • Model artifact lineage and metadata
  • And many more…

How Does MLOps Help?

MLOps describes the operational framework, unique to your organization, that maximizes the quality and usefulness of data science.

The phases of an MLOps framework are often described in terms of the machine learning (ML) development lifecycle. But this occludes the big picture and obfuscates the nitty-gritty details. We need a better way of describing the value of MLOps.

In our experience, at a high level, the value can be attributed to three catagories:

  • Governance allows organizations to manage and control risk throughout the ML development lifecycle. From audit trails that show which models are used where to evidence that a model has been signed off for production deployment. Banks are really good at this form of MLOps because they have regulatory requirements that force them to do so. But organizations everywhere can leverage the same techniques to reduce risk.
  • Provenance is often described as the ability to track a lineage, from a deployable artifact to the data it originated from. But delivering provenance provides robust, repeatable pipelines. Provenance promotes devops and gitops; proven cloud-native techniques. And provenance provides uniformity - you’ll find that common patterns are reused, and operational simplifications because of the uniformity.
  • Operational automation helps reduce the toil involved with running ML models in production. This idea is less general, and more specific. Precisely what you automate and how you do it depends on various non-functional requirements, like the size of your team, or how popular the services are. But the benefits are universal. If you can automate a dangerous or tedious part of the process, this reduces the risk of mistakes, enforces compliance and reduces the operational burden on engineers and data scientists

How to ML Deployment Pipelines Relate to MLOps?

ML deployment pipelines are necessary to provide robust, repeatable procedures for managing your models.

They are one of the most important parts of moving a trained model to a place where it can be consumed by downstream applications or users. This means that in many of our MLOps development projects this is one of the first areas our experts tackle.

But remember that it only forms a small part of your overall MLOps strategy. Other phases that fall under the MLOps banner, like training, monitoring, provenance, and data versioning can be just as important.

Ultimately its importance depends on your unique circumstances and ML workload. Winder.AI are experienced MLOps consultants that can help you make the right decision.

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MLOps Services

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MLOps Consulting

MLOps Consulting

Do you need help starting your MLOps journey, or do you currently have operational ML or RL problems?

Winder.AI provides expert evaluation and guidance to improve your MLOps systems and process. We advise organizations both large and small and operate across the world including Europe, UK, and USA.

MLOps Development

MLOps Development

Do you lack the time or resources to implement your MLOps vision?

Our MLOps engineers have had years of experience designing, building, and operating MLOps systems for some of the worlds largest companies. Learn more.

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From startups to the world’s largest enterprises, companies trust Winder.AI.

Selected Case Studies

Some of our most recent work. You can find more case studies in our portfolio.

Using Reinforcement Learning to Attack Web Application Firewalls

Introduction Ideally, the best way to improve the security of any system is to detect all vulnerabilities and patch them. Unfortunately this is rarely possible due to the extreme complexity of modern systems. One primary threat are payloads arriving from the public internet, with the attacker using them to discover and exploit vulnerabilities. For this reason, web application firewalls (WAF) are introduced to detect suspicious behaviour. These are often rules based and when they detect nefarious activities they significantly reduce the overall damage.

Helping Modzy Build an ML Platform

Winder.AI collaborated with the Modzy development team and MLOps Consulting to deliver a variety of solutions that make up the Modzy product, a ModelOps and MLOps platform. A summary of this work includes: Developing the Open Model Interface Open-sourcing chassis, the missing link that allows data scientists to build robust ML containers Model monitoring and observability product features MLOps and model management product features The Problem: How to Build An ML Platform Modzy’s goal is to help large organizations orchestrate and manage their machine learning (ML) models.

How To Build a Robust ML Workflow With Pachyderm and Seldon

This article outlines the technical design behind the Pachyderm-Seldon Deploy integration available on GitHub and is intended to highlight the salient features of the demo. For an in depth overview watch the accompanying video on YouTube. Introduction Pachyderm and Seldon run on top of Kubernetes, a scalable orchestration system; here I explain their installation process, then I use an example use case to illustrate how to operate a release, rollback, fix, re-release cycle in a live ML deployment.

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The team at Winder.AI are ready to collaborate with you on your mlops project. We will design and execute a solution specific to your needs, so you can focus on your own goals. Fill out the form below to get started, or contact us in another way.