Data Science POCs

De-risk your data science projects with the ultimate data science POC.

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Your Data Science POC Company

Data science projects are risky.

A significant amount of the time, some suggest around 80%, data science projects fail. That’s not because there is a failure, in the structural sense of the word, but because the data or the domain prevents you from achieving your goals.

The problem isn’t failure, though. The problem is that it takes too long to find out that a data science project will fail. Our proof-of-concepts (POCs) de-risk a project by researching and investigating key risks, before wasting time on development, coding, hardening, etc.

Although all of our POCs differ, they generally deliver a rough prototype to prove that the riskiest parts of the project will be successful, or not. Data science POCs generally take the form of a report or a set of results that demonstrate that an idea is technically feasible, given the data and current technology capabilities.

Our data science POCs are then promoted into a machine learning development project, where we design, build, and deliver practical artificial intelligence solutions.

Our Approach to Data Science POCs

Successful POCs arise from decades of experience. Take a look at our data science POC process.

Data Science POC 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.

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 Exploration

Using the domain knowledge, we gather even more insight by exploring your data. You never know what you’ll find!

5. Data 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 Exploration

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.

7. Model Analysis

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.

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.