Data Science Research

Delivering paradigm-shifting industrial research to organizations around the world.

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Data Science Research Company

Our industrial data science research services allow you to perform game-changing research, leveraging a team of PhD data scientists, without having to maintain an expensive data science department.

Organizations like Nestle and Microsoft leverage our data science expertise to augment their development teams. They find that their ambitions often outstrip their resources and Winder Research helps them move faster.

Winder Research is an industrial data science research company that specializes in delivering practical, pragmatic solutions to the most complex of data science challenges. Our data scientists experts have collectively published several books and tens of high-profile research papers, although much of our work is under NDA. Rest assured that we will immerse ourselves in your business to discover and solve the key problems you are facing today.

If you are looking to invest in an industrial data science research program, then I encourage you to contact Winder Research first, to learn from our experience.

Our Approach to Data Science Research

Successful research projects arise from decades of experience. Take a look at how we deliver data science research.

Our Data Science Research 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. Domain Knowledge Transfer

Businesses are often experts in their own domain. This domain expertise is valuable to help direct future solutions. Literature reviews ensure we start with a reasonable baseline.

3. Problem Definition/Clarification

POCs usually start with a vague idea of what problem they are trying to solve. But the problem definition often changes over time, becoming more concrete, adapting to what is possible given the data.

4. Data Capture/Generation

One thing that makes data science research projects unique is that they often don’t have the right data to begin with. Significant effort is often spent capturing new data that more efficiently solves the problem.

5. Data Exploration and Analysis

In this phase expert data analysts extract knowledge from the data. This often leverages actionable insight and is used to validate whether the solution is viable.

6. Model Development and Evaluation

After the data analysis validates that the idea is sound, an initial phase of model exploration is intended to validate whether the problem can be automated. After this phase the models are analysed to ensure they are performant enough to suggest viability. Note that even at this late stage it is sometimes necessary to revisit the problem definition.

7. Solution Evaluation

In an industrial context, it is important to include a high-level evaluation of how well a solution actually solves the business problem. This is the key issue with academic researchers; their solutions are not pragmatic enough to work in the real world.

8. Reporting

Once models are validated then it’s time to report the results back to the stakeholders. After this phase we often start looking at another problem, or promote it to a fully-fledged machine learning development project.

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 Research’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 Research.

Selected Case Studies

Some of our most recent work. 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 Research 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.