Reinforcement Learning Development

The World’s First and Finest Reinforcement Learning Engineers

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 Trust Winder.AI

We've worked with hundreds of amazing people, all over the world.

  • Machine learning product development for Google.
  • Kubeflow consulting for Microsoft.
  • MLOps consulting and development for Shell.
  • Deep reinforcement learning consulting and development for Nestle
  • MLOps product development for Canonical.
  • MLOps consulting for Docker
  • MLOps consulting for Ofcom
  • MLOps product development for Grafana.
  • MLOps consulting for Stability.AI
  • Authors of a Reinforcement learning book with O'Reilly
  • Data science lecturing with Pearson
  • Machine learning integration for Pachyderm.
  • Vendor MLOps product development for Modzy.
  • MLOps consulting for Neste.
  • Deep reinforcement learning consulting for CMPC.
  • Deep reinforcement learning consulting for Novelis.
  • Reinforcement learning consulting for Genesis
  • MLOps consulting for Lightning.AI
  • AI product development for Protocol Labs
  • MLOps consulting for Tractable
  • MLOps consulting for Interos.AI
  • MLOps consulting for Ultraleap
  • MLOps consulting for AICadium
  • DAS and digital signal processing for OptaSense
  • DAS and digital signal processing for Focus Sensors.
  • DAS and digital signal processing for Frauscher
  • MLOps consulting for Living Optics

Selected Case Studies

Some of our most recent work. You can find more in our portfolio.
MLOps in Supply Chain Management

Case study

MLOps in Supply Chain Management

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.

Announcing Stable Audio: A Generative AI Music Service

Case study

Announcing Stable Audio: A Generative AI Music Service

We’re pleased to announce the release of Stable Audio, a new generative AI music service. Stable Audio is a collaboration between Stability.AI and Winder.AI that leverages state-of-the-art audio diffusion models to generate high-quality music from a text prompt.

MLOps in Insurance

Case study

MLOps in Insurance

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.

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.

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