Machine Learning Consulting and Development

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What is ML?

Machine learning automates decisions.

What Is Machine Learning?

Machine learning (ML) is the process of teaching algorithms to make decisions, based upon data. It encompasses a range of sub-domains like reinforcement learning and plays a crucial role in the discipline of data science.

Organizations use ML primarily to automate decisions that humans would otherwise perform. The major benefit of outsourcing decisions to ML is that it is quantifiable and enforced.

Humans are free to make their own decisions, which is both a blessing and a curse. It is problematic when different people make decisions based upon their own rules.

Take an application form, for example. If humans applied their own judgement then two different people would make two different application decisions. ML encodes the knowledge of all humans to make an unbiased, systematic, traceable assessment.

The downside of ML, however, is that it is hard to encode exceptions. Humans are quick to react to exceptional circumstances, but ML often struggles because it hasn’t observed enough of these states. In these cases human-in-the-loop approaches are beneficial.

What is Machine Learning Not?

Machine Learning is not Artificial Intelligence

Artificial intelligence (AI) is an academic philosophy debating human consciousness. When does a programmed machine become human-like?

Machine learning (ML) is the specific process of teaching an algorithm based upon data. It can only learn from the data you provide it. It does not “think”.

ML in industry is generally considered to be an extension of an “expert system”, which is an application that is specifically designed to perform one task with super-human precision.

This means that ML solutions are targeted. They are designed by experts to automate a specific problem.

ML is not Guaranteed

ML learns to make a decisions based upon the data it can observe.

If you don’t have the data, this makes it hard to create an accurate algorithm. Or if the data isn’t related to the problem, then again, that makes it hard to solve the problem. Not impossible, because it’s often possible to simulate data, but hard.

This makes every new engagement difficult to estimate, because it often depends heavily or prior work and data. We’ve worked on many projects where we’ve tried and failed because the data wasn’t available, didn’t provide the information the client wanted, or on more than one occasion, academic papers were incorrect!

How Does ML Help?

Machine learning (ML) helps businesses automate decision making processes.

As well as optimizing current business operations, ML can also lead to new products and features. For example, the finance industry has developed a wide variety of ML models to automate routine tasks (e.g. FinTech). Whereas the Technology industry enables whole new industries through novel applications of ML (e.g. Google).

ML automates single-shot, point-in-time decisions and forecasts. Single-shot means that the algorithm is trained to make correct predictions for that single observation only. For example, predicting that a picture of a t-shirt is of a class named t-shirt, or predicting the cost of some component in 6 months time.

ML does not optimize over multiple steps. For example, if you’re attempting to predict what product to show next on a website, then that item depends on what the user has previously browsed. If you used ML in this case, the ML algorithm would suggest showing the same second item, no matter what the user browsed first. Reinforcement learning is used in these cases.

In our experience developing ML solutions for organizations like Google, Microsoft, and shell, given the right situation, there are a number of benefits ML can provide:

  • Automation of decision intensive tasks: We’ve worked in the distributed acoustic sensing (DAS) market for a long time. These are systems that produce vast quantities of audio data. You can classify signals by eye, but there is just too much data to do that consistently all the time. ML automates this process to produce activity detection classifiers. This idea can be applied to all other industries and domains.
  • Consistent, quantitative decision making: One key problem when scaling a business is that different people make different decisions. Using ML to make a decision allows you to make decisions more consistently, faster. It’s also hard to quantify human errors and biases, whereas this is standard practice within ML.
  • New product lines: Many companies exist because outsourcing a business or user function to a service is easier than trying to do it themselves. If you spot a situation where you own or can collect a large amount of data about a task, it’s often possible to leverage that to offer new products or services. The key, however, is finding a problem that is worth solving. This is the principal reason why ML projects “fail”. Solving a problem that doesn’t need solving.
  • Reducing operational burden: We’ve work on some projects that aim to reduce the operational burden of a business. For example, we worked with Shell to build a domain-specific question and answering tool to answer questions like “what valve fits onto a T-2304”. If you can automate common tasks or decisions, this gives free time back to your staff, to concentrate on more important tasks like product development or sales. The key with this type of work is to find ways to make their life easier, not attempt to automate them away.

The World's Best AI Companies

From startups to the world’s largest enterprises, companies trust Winder Research.

Machine Learning Consulting Services

Winder Research helps companies build production-quality machine learning products and platforms.

Machine Learning Consulting

Winder Research are industrially renowned experts in machine learning (ML).

Companies like Source Digital work with us to provide expertise where they need it most.

Our consultative 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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Machine Learning Research and Development

Winder Research predominantly works on projects that involve developing ML solutions for domain specific problems.

Take Shell, who are one of the world’s largest energy companies, as an example. We’ve worked for many years in different parts of the organization solving specific ML challenges. We built a whole NLP platform which was capable of solving complex problems like question answering and automatic reporting.

We work with start-ups and small-sized companies too. One project for Industrial Computing involved us performing significant data collection, analysis and ml development to deliver a window open detection algorithm for an IoT heating controller.

We can help you too, no matter what industry you are in. We operate under all contract types, from fixed cost proof-of-concepts to ongoing time and materials expertise.

Whatever your business, we can help.

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Winder Research’s ML implementation for Grafana - Courtesy of Grafana

Machine Learning Product Development

The team at Winder Research are experienced ML practitioners and researchers.

Vendors of ML products can take advantage of our expertise to help them deliver their product. People like Grafana did this to create their new ML-driven monitoring capability, which required designing a bespoke integrated MLOps solution from scratch. As leaders in this space We’ve also helped Modzy and grid.ai to build out their platforms and offerings.

Winder Research is able to deliver fully self-managed incremental product improvements. This alleviates the burden from your team and shortens development timelines. Our experts can also integrate tightly with your ways of working for a collaborative solution.

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Selected Case Studies

Some of our most recent work. Find more in our portfolio.

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.

How We Built an MLOps Platform Into Grafana

Winder Research collaborated with Grafana Labs to help them build a Machine Learning (ML) capability into Grafana Cloud. A summary of this work includes: Product consultancy and positioning - delivering the best product and experience Design and architecture of MLOps backend - highly scalable - capable of running training jobs for thousands of customers Tight integration with Grafana - low integration costs - easy product enablement Grafana’s Need - Machine Learning Consultancy and Development Grafana Cloud is a successful cloud-native monitoring solution developed by Grafana Labs.

Improving Data Science Strategy at Neste

Winder Research helped Neste develop their data science strategy to nudge their data scientists to produce more secure, more robust, production ready products. The results of this work were: A unified company-wide data science strategy Simplified product development - “just follow the process” More robust, more secure products Decreased to-market time Our Client Neste is an energy company that focuses on renewables. The efficiency and optimization savings that machine learning, artificial intelligence and data science can provide play a key role in their strategy.