Reinforcement Learning Consulting

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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 automates 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

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.

A Comparison of Computational Frameworks - Spark, Dask, Snowflake and more...

Winder.AI worked with Protocol.AI to evaluate general purpose computation frameworks. A summary of this work includes:

  • Comprehensive presentation evaluating the workflows and performance of each tool
  • A GitHub repository with benchmarks and sample applications
  • Documentation and summary video for Bacalhau documentation website

Save 80% of Your Machine Learning Training Bill on Kubernetes

Winder.AI worked with Grid.AI to stress test managed Kubernetes services with the aim of reducing training time and cost. A summary of this work includes: Stress testing the scaling performance of the big three managed Kubernetes services Reducing the cost of training a 1000-node model by 80% The finding that some cloud vendors are better (cheaper) than others The Problem: How to Minimize the Time and Cost of Training Machine Learning Models Artificial intelligence (AI) workloads are resource hogs.

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.

Recent Reinforcement Learning Articles

Find more articles in our blog.

Reinforcement Learning Presentation: Cyber Security

Dr. Phil Winder shares experiences of Winder.AI’s reinforcement learning consulting experience at a variety of large and small organizations. Abstract In this talk he focuses on RL applications, looking at the use of RL in cyber security and discusses one interesting case study about how Winder.AI helped an internal security team develop a tool to hack web application firewalls. About This Series Welcome to Winder.AI talks. A series of free interactive webinars hosted by Dr Phil Winder, CEO of Winder.

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.

Automating Cyber-Security with Reinforcement Learning

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. The common suggestion is to test for security, often leveraging the expertise of security-focussed engineers or automated scripts. But there are two fundamental issues with this approach: 1) security engineers do not scale, and 2) scripts are unlikely to cover all security concerns to begin with, let alone deal with new threats or increased attack surfaces.

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.