Emerging Technology

Feature Stores

Scaling machine learning for business transformation

Feature Stores

overview
business drivers
how we help

Feature stores will do for machine learning what the cloud did for big data.

Businesses are on the cusp of a Cambrian explosion in the field of machine and deep learning.

However, this will be stalled without the ability to scale AI and one of the biggest obstacles to overcome, is how to meet the demands of the business in a timeframe and cost that is compelling.

To truly leverage data assets, businesses need to collapse the time it takes to train, deploy and manage their machine learning models.

Failure to adopt feature store technology could affect your ability to compete or maintain and grow market share.

Our Expertise

Our data platforms practice specialises in data strategy, technology delivery and adoption as well as transition to data operations. They will help you to scale out your machine learning ambitions whilst enhancing your exisiting investments, helping you to comply with increasing compliance and regulatory requirements.

We bring the right blend of both delivery expertise and innovative data solutions to solve our clients’ most pressing needs.

We will establish your feature store readiness by looking at where you are today and what the opportunities are to leverage machine learning in order to propel your business forward.

What sets us apart is our ability to define a strategy which is deliverable, not just what you want to hear.

 

 

What are the business drivers?

The feature store is a centralised feature hub where you curate all the features, data and decisions that your business has ever made.

Improve decision making

Having a feature store will allow insight into what data is important and common across your machine learning models.

This will give insight into where to invest in data collection and data quality to further improve your data and business decision-making quality.

Increase ability to scale

As common machine learning archetypes start to evolve, being able to scale your data management capability to server consistent feature sets is vital.

Feature stores allow your machine learning team to scale up considerably, investing in data curation and collection, whilst creating a solid foundation to manage both your training data and feature serving.

Create monetisation potential

The ability of feature stores to define and store large amounts of highly curated features and track change over time, represents a potentially lucrative opportunity for both the machine learning teams and the wider organisation.

This data can then be packaged and sold as a feature service.

Improve decision fidelity

In an age of increasing data governance, feature stores should form an integral part of your organisation’s governance model, tracking feature drift and highlight areas of concern proactively.

The ‘decision vault’ that a feature store provides will ensure decision fidelity, allowing you to justify historical business decisions.

Improve business efficiency

Having a feature store could revolutionise the resource you have by placing less burden on your data science team, freeing up time which can be deployed training models elsewhere in the business.

How we help

Drawing on the expertise of our DevOps and DataOps specialists, we will guide you on your journey towards the successful scale out of machine learning in your business, using the feature store.

01

Preparation (2 weeks)

The first phase of this process is to prepare your business for the trialling of feature store technology. It’s about a rapid assimilation of your data estate to identify candidate data sources for feature generation. This process prepares the environment and data required to target a set of models that will be used in the trialling of the feature store technology. As part of this, you will participate in workshops and bring specific problems, issues and needs to the table so that we can craft a comprehensive set of success criteria for a feature store trial.

02

Trial (8 weeks)

The aim of this phase is to immerse your data engineers and data scientists in best practice from data pipelines, feature management and operations, to enable feature store implementation and management. This will provide the insight into how a feature store- enabled way of working can accelerate and scale your machine learning deployments. As part of this, you would deploy data engineers and data scientists to the trial project so they get hands on experience and are part of the trial team.

03

Decision (2 weeks)

This stage is all about deciding on the way forward. We provide the roadmap, costs and timelines needed to fully deploy a feature store in your business. This will inform your decision on whether or not to progress with a feature store. You would need to dedicate a project manager to work alongside our team for a two-week period to work through the roadmap, costings and final report.

Insights from our experts

Gary Richardson
MD, Emerging Technology
September 25, 2019

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On 11th September we unveiled our Artificial Intelligence: Hype vs Reality report. The event, took a deep dive into the findings of the report and the learnings that can be applied across enterprise when it comes to scaling AI adoption. For those who were unable to join us, here is a quick recap and we hope to see you at future events. 

AI Big Data Emerging Technology Machine Learning
Gary Richardson
MD, Emerging Technology
September 11, 2019

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In partnership with YouGov, we commissioned a short survey of over 1,000 senior decision makers, including owners, partners, chairpersons and non-executive directors, working across the public and private sectors. The focus of the study is to lift the veil on boards’ attitudes towards Artificial Intelligence and understand the reality of adoption and awareness at executive level.

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6POINT6
May 23, 2019

Scaling AI in the Enterprise Recap

Thanks to those who were able to join us last week for our first AI event with InstaDeep. This, the first of four events, looked at Scaling AI in the Enterprise and helping business leaders to realise the potential of introducing and scaling AI in their businesses. For those who were unable to join us, here is a quick recap and we hope to see you at future events.

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Gary Richardson
MD, Emerging Technology
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Dr. Daniel Alexander Smith
Lead Big Data Architect
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Getting more insight and value from a business’s data, for example through machine learning, first requires a considered view of the data estate. An increasingly popular method is to build a knowledge graph of all data assets in the business.

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Dr. Daniel Alexander Smith
Lead Big Data Architect
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