MLOps Consulting, Implementation, and Development

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What is the Meaning of MLOps?

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 Research are experienced MLOps consultants that can help you make the right decision.

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The World's Best AI Companies

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

MLOps Consultancy Services

Winder Research helps companies build production-quality MLOps products and platforms.

MLOps Consulting for Living Optics - Courtesy of Living Optics

MLOps Consulting

Investing early to make the best MLOps decisions potentially saves millions in costs.

One of our clients, Neste, invested in designing and building an operational MLOps handbook for their organization, which paired with their bespoke GCP-focussed ML tool-suite. They are now leveraging this to great effect and have rapidly deployed over 20 ML projects since then.

We’re also helping companies like Living Optics, a start-up which is building an MLOps platform for the first time. They appreciate our guidance and lack of bias to deliver a design that is best suited to their unique needs. In this case their unique need is a massive amount of one-time data that is required during training, and very long training times. This required a focus on training observability and optimizing the placement of data.

Winder Research provides expert evaluation and guidance to improve your ML development and de-risk implementation details. We advise organizations both large and small and operate across the world including Europe, UK, and USA.

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MLOps Implementation and Operational Support

Creating robust production operational ML infrastructure and components takes a lot of work.

There are a huge number of potential options and the list is growing exponentially. Being independent, we work with all cloud and MLOps vendors. We also promote the use of open-source alternatives where possible. In any case we tailor our approach to your unique needs and requirements.

We’ve helped some of the world’s largest businesses build and operate their artificial intelligence (AI) platforms that make MLOps a priority.

This public video from Microsoft demonstrates some of our work with Shell.

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Winder Research’s MLOps implementation for Grafana - Courtesy of Grafana

MLOps Product Development

The team at Winder Research are both experienced ML practitioners and MLOps champions.

Vendors of MLOps products can take advantage of our expertise to help them deliver their product. People like Grafana did this to create their new ML-driven monitoring capability, which required designing a bespoke integrated MLOps solution from scratch. As leaders in this space We’ve also helped Modzy and grid.ai to build out their platforms and offerings.

Winder Research is able to deliver fully self-managed incremental product improvements. This alleviates the burden from your team and shortens development timelines. Our experts can also integrate tightly with your ways of working for a collaborative solution.

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Selected Case Studies

Some of our most recent work. Find more in our portfolio.

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.

How We Built an MLOps Platform Into Grafana

Winder Research collaborated with Grafana Labs to help them build a Machine Learning (ML) capability into Grafana Cloud. A summary of this work includes: Product consultancy and positioning - delivering the best product and experience Design and architecture of MLOps backend - highly scalable - capable of running training jobs for thousands of customers Tight integration with Grafana - low integration costs - easy product enablement Grafana’s Need - Machine Learning Consultancy and Development Grafana Cloud is a successful cloud-native monitoring solution developed by Grafana Labs.

Improving Data Science Strategy at Neste

Winder Research helped Neste develop their data science strategy to nudge their data scientists to produce more secure, more robust, production ready products. The results of this work were: A unified company-wide data science strategy Simplified product development - “just follow the process” More robust, more secure products Decreased to-market time Our Client Neste is an energy company that focuses on renewables. The efficiency and optimization savings that machine learning, artificial intelligence and data science can provide play a key role in their strategy.