Reinforcement Learning Development

The World’s First and Finest Reinforcement Learning Engineers

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Dedicated Reinforcement Learning Engineers

Winder’s engineers are world leading experts in reinforcement learning. We wrote the book on reinforcement learning!

Our focus and specialism on reinforcement learning development has allowed us to deliver RL projects to some of the worlds largest and leading AI companies. They leverage our reinforcement learning services to design, prove, and deploy production reinforcement learning products.

Reinforcement Learning Development Services

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

Our book on industrial deep reinforcement learning that we use as part of our development.

World Leading RL Company

Winder.AI are industrially renowned experts in reinforcement learning (RL) and we can help you with your RL problem.

Companies like Nestle work with us to provide expertise where they need it most. Our consulting guidance helps you complete your project faster and to a higher quality that it would have been otherwise. Our flexibility allows us to integrate tightly with your ways of working.

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Our Approach to Reinforcement Learning Development

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

Reinforcement Learning Development Process

1. 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.

2. Domain Knowledge Transfer and Infrastructure Setup

Businesses are often experts in their own domain. This domain expertise is valuable to help direct future solutions. In this phase we also ensure any prerequisites are available, including working with our very own ML engineers and MLOps consultants to ensure the infrastructure meets our needs.

3. MDP Refinement

The definition of the MDP is crucial in RL projects. We often iterate over the MDP design to help improve performance.

4. Environment Development/Refinement

The environment, whether in simulation or in real life, needs refinement. Accurate simulations help improve the sim2real problem and updating environments to incorporate new information can significantly boost performance.

5. RL Data Analysis/Refinement

Like much of data science, understanding and appreciating the data is important. Refining what the agent can “see” significantly improves learning performance.

6. RL Algorithm Development

Working on the actual RL algorithm takes a surprisingly small amount of development time, but it is often necessary, especially when improving policy models.

7. Agent Evaluation and Analysis

Thorough and robust evaluation practices are vital for directing development. These results are often shared with stakeholders as a representation of progress. Note how we often iterate back to the MDP refinement to apply new learnings.

8. Deployment and Monitoring

In the final phase we deploy and operate our agents. Do not underestimate this phase; there are a lot of pitfalls, especially when operating at scale. We collaborate with our very own expert team of MLOps consultants to help here.

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 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 RL Development Project Now

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