Reinforcement Learning Consulting

Welcome to a New Age of Industrial Automation using Intelligent Agents

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The Value Proposition of Reinforcement Learning

Strategic decisions tend to be the most lucrative decisions a business can make and therefore they are typically the most expensive. Reinforcement learning is a technique that we pair with AI consulting to automate strategic decisions.

The value proposition of reinforcement learning.
The value proposition of reinforcement learning, courtesy of our Reinforcement Learning book.
An image showing how reinforcement learning learns.
How reinforcement learning really works, courtesy of our Reinforcement Learning book.

How Reinforcement Learning Really Works

The key premise in reinforcement learning are the concepts of an environment and a policy.

The environment represents the space that the agent will operate within, with all the signals and data it can observe. The policy represents the internal decision model of the agent, which is responsible for choosing what to do within the environment.

Over time, the agent learns to update it’s internal model of the world to make better decisions.

Reinforcement Learning Consulting 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 consulting.

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 Consulting

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

Reinforcement Learning Consulting Process

1. Business Context

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.

2. Stakeholder Inclusion

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.

3. 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.

4. Domain Knowledge Transfer

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

5. Data Analysis

Any reinforcement learning consulting involves a limited amount of actual data science. In this phase expert data analysts extract knowledge from the data. This often leverages actionable insight.

6. Research and Validation

Following analysis, some time is often spent validating results or confirming value.

7. Ethical/Legal/Safety Analysis

Any work that arises from the consulting should be de-risked in terms of ethical concerns, legal ramifications, or safety risks. This step depends on the industry, the domain, and the context of the problem.

8. Strategic Solution

A solution is presented back to key stakeholders. In many projects we then move into an implementation phase. Other times we step back and perform another iteration to find a better problem.

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.

Recent Reinforcement Learning Articles

Find more articles in our blog.
LLMs: RAG vs. Fine-Tuning

Generative AI

LLMs: RAG vs. Fine-Tuning

Large language models are applicable to a wide variety of AI problems and many leverage private data to enable bespoke use cases. But how do you best take advantage of that data?

LLM Prompt Best Practices For Large Context Windows

ChatGPT

LLM Prompt Best Practices For Large Context Windows

This article delves into the nuances of using large language models (LLMs) with large context windows, highlighting the benefits and challenges they present, from enhancing coherence and relevance to demanding more computational resources. Learn practical strategies for prompt design, maintaining narrative coherence, and utilizing attention mechanisms effectively.

Interview: How The EU AI Act Was Born With Javier Campos

EU AI Act

Interview: How The EU AI Act Was Born With Javier Campos

In this Webinar our CEO Phil Winder sat down with Javier Campos to discuss the EU AI Act. He is chief innovation officer at Fenestra, and is the author of “Grow Your Business with AI: A First Principles Approach for Scaling Artificial Intelligence in the Enterprise”, published by Apress. Javier was involved in the development of the EU AI act, and was also involved in the development of the EU Cookie Law in the early 2010s.

Start Your RL Consulting Project Now

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