MLOps Development

Let’s unblock your data science by building your perfect MLOps system

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MLOps Development Company

If you’re looking for MLOps experts to help build your MLOps system, you’re already well on your way, so we won’t repeat why MLOps is necessary.

Instead, you’re probably struggling to find the time or resources to put your plan into action. Let us help you there.

We’ve been working with a real mix of startups and global mega-corporations. They both have very different needs, but they have the same goal: to make it easier, faster, cheaper, and more robust, when delivering machine learning and reinforcement learning projects.

By choosing Winder.AI as your trusted MLOps partner, rest assured that you talking to the most experienced MLOps agencies in the world. Together with our friends at the MLOps community, we helped define what MLOps is today. If you search around you can probably find our CEO Phil Winder talking about “DataDevOps”; this was our moniker for MLOps before MLOps was a thing!

Since then we’ve helped companies as large as Shell, with hundreds of data scientists, to start-ups like Living Optics, with only a few. Our flexibility allows us to collaborate closely with our clients. And our experience delivering ML and RL solutions demonstrates that we eat our own dog food.

Winder.AI is a global data science consultancy company based in the UK. Our MLOps consultants deliver incredible value by evaluating and recommending strategic business decisions to further your organizational ambitions.

MLOps Development Services

Winder.AI helps companies build production-quality reinforcement learning products and platforms.

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.AI’s MLOps implementation for Grafana - Courtesy of Grafana

MLOps Product Development

The team at Winder.AI 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.AI 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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Our Approach to MLOps Development

Successful development projects arises from decades of experience. Take a look at our tips for a successful MLOps development project.

A flowchart depicting our MLOps development process.

MLOps Development Process

1. Business Context and Stakeholder Inclusion

Any problem demands context from the business. A solution for one industry may not be applicable to another, nor is every business the same. Establishing shared context helps get the project off to the right start.

More than just identification, we find projects are optimal when key stakeholders are including in the project. With “skin in the game”, stakeholders are far more likely to collaborate to produce a better overall solution.

2. Problem Definition

A key phase where business problems are defined and prioritized. It is worth spending time to get this right, as subsequent effort could be ineffective and wasted.

3. Domain Knowledge Transfer

Businesses are often experts in their own domain. This domain expertise is valuable to help direct future solutions.

4. Process Gap Analysis

In this phase our expert consultants speak to all the stakeholders and users to find the largest efficiency losses and pain points in the ML productionization process.

5. Research and Validation

Finally we get to the phase where we begin to design a solution, based upon the requirements for the MLOps system gathered earlier in the process. In some cases we also provide POCs to validate these solutions.

6. Agile Development and Implementation

Development and implementation phases usually split off into agile development flows. We usually collaborate with the wider engineering team and join their cadences.

7. Early Adopter Testing

As soon as the solution is stable enough for testing, we bring in the early adopters (often the stakeholders) to start using the system. Quickly we find the flaws and improve, before rolling out more widely.

8. Roll Out and Operation

Finally we roll out the solution to the rest of the users and organization. For some organizations we actively operate these systems too.

Optimizing for Value Generation

Businesses have three core operational functions. Processes define how businesses run. Decisions decide when businesses are run. Strategies define why businesses are run.

Software has successfully automated many business processes. Data science automates decisions and strategies via machine learning and reinforcement learning, respectively.

By leveraging our data science consulting services we can help you automate the top two most valuable tiers in the pyramid, to make your organization more efficient and profitable.

The value of reinforcement learning, courtesy of our Reinforcement Learning book.
The OODA loop for continuous innovation.
Winder.AI’s data science consulting strives for continuous innovation. Courtesy of our Reinforcement Learning book.

Continuous Innovation

The infamous OODA loop, originally developed by the US military, is of particular use during our work because it helps promote innovation.

At every phase we look for opportunities to add value and make your products and services better. Our clients find that our work greatly exceeds their expectations due to the extra value presented by our solutions.

The World's Best AI Companies

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.

Start Your MLOps Development Project Now

The team at Winder.AI are ready to collaborate with you on your mlops development 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.