Drive real value from your machine learning investments,
moving past PoC to productionised efficiency.

39% of businesses are accelerating their ML projects due to COVID-19.

Businesses are expecting to see a 10X return on their investments.

Lack of in-house skills is the #1 challenge for starting ML projects.

Fraud detection leads the way in the use case race.

Appsbroker Machine Learning Report 2020

Appsbroker Launch 2020/21 Benchmarking Survey for Machine Learning

The only UK-based machine learning report conducted during the COVID-19 pandemic.

  • 39% of ML initiatives are being accelerated due to COVID-19
  • 57% of companies will spend >£1m on ML in the next 24 months
  • Fraud detection and process automation lead the way
Use Case

Demand Forecasting

Predicting demand is the crux for many organisations; whether that’s retail sales or manufacturing product volumes. Obtaining accurate predictions is a simple way to deliver efficiencies using machine learning.


  • Increase customer satisfaction/sales and optimise stock
  • Incorporate external factors to better predict demand
  • Build a robust forecasting system which can react quickly


purple sky with graph
Use Case

Personalisation & Product Recommendation

Generating tailored product recommendations, offers and experiences for customers is key to maintaining a personalised touch across e-commerce, traditional retail, financial services, and more.


  • Increase customer satisfaction through personalisation
  • Enhance customer engagement by selling more
  • Improve overall quality of service across your business


pins on a board with string
Use Case

Fraud Detection

Fraudsters employ sophisticated tactics to make money from unsuspecting businesses. Machine learning systems must now be modernised to include monitoring, automated retraining and an MLOps framework for automated redeployment.


  • Enhance fraud detection systems through automation
  • Reduce fraud loss using state of the art algorithms
  • Automatically react to fraudulent behaviour


Magnifying glass and graphics
Use Case

Image Processing

Applying ML to image processing has become a reality in recent years, whether that’s in medicine, geological surveys, or self-driving vehicles. Google Cloud Vision has further simplified development costs.


  • Systemise image expertise to make it organisational
  • Scale up image processing capabilities using algorithms
  • Task machines with laborious work instead of humans


machine learning face recognition
Use Case

Natural Language Processing

Provide solutions with human-level language understanding across case working automation, email handling or smart assistants. Harness machine learning to automatically review text-based applications and make rapid decisions.


  • Apply analytics to numerical and text-based data
  • Automate tasks and reduce costly human interactions
  • Develop organisational knowledge and expertise


natural language processing inside cogs

A Lightning Guide to Machine Learning

This year we’ve seen a huge shift in business priorities in the UK and across the globe. There’s widespread consensus that a handful of key enabling technologies will be the catalyst for the next phase of exponential commercial progression.

Whether you’re looking to deliver efficiencies, specific outcomes or simply unlock the power of choice – centre stage among these technologies is machine learning.

Download the white paper – part 1 of 2

Appsbroker Machine Learning White Paper 3D

4 Steps to Drive Value from Your Machine Learning Investment


Focus on solving key business challenges.


Identify ROI opportunities and follow the money.


Align to business operations that are changeable.


Ensure you have a sufficient pool of data.

MLOps – DevOps for Machine Learning

One of the most difficult aspects of machine learning is integrating it with software engineering and building out a CI/CD pipeline for your ML solutions.

At Appsbroker, we have the skills to help you productionise your machine learning – whether that’s taking a Jupyter notebook live or building a scalable and resilient API for low latency inference.

Ethical, Fair and Regulatory Compliant AI

A key challenge when adopting machine learning is ensuring your algorithms are ethical and comply with regulations.

If you haven’t considered how GDPR might affect you, or you want to ensure your algorithms don’t discriminate against certain demographics, or you need to be able to explain your decisions to a regulatory, we can help.

Meet Our Machine Learning & AI Team

Matthew Penton

Head of Data & Analytics

Matthew Penton at Appsbroker wearing purple branded lanyard

Contact Us
Start your data modernisation journey today.

Fill out the form and we’ll schedule a meeting to discuss your requirements in more detail.