Business leaders rely on data and analytics to decide and accelerate business initiatives. It’s hard to make difficult decisions confidently when 65% of decisions made are more complex than they were two years ago, according to Gartner. Machine learning solutions can help your organisation uncover new insights and make more informed decisions by improving your ability to sift through alerts as well as identify threats and trends.  In this panel, you will learn how to build pipelines and AI systems, why data-centric machine learning can be valuable, and more.  Learn more about: - The need for data-centric machine learning that prioritises data quality and pipeline robustness - How to build pre-packed pipelines for specific industry use cases - How to build strong AI systems from modular, weak AI pipelines and components - Key technological and organisational challenges that impact the success of machine learning projects - And much more!  Speakers: - Eric Topham, CEO & Co-Founder at The Data Analysis Bureau - Rajdeep Biswas, Director, Advanced Analytics & Machine Learning at Microsoft - Nik Spirin, Co-founder & CEO at Metapixel AI

In the second and final episode of the project Predicting UTI Risks in Care Homes for Early Detection that we developed with the Person Centred Software and Imperial College, London, our students and researchers explain their findings, methodology and areas of implementation of their research.

In this episode of The Innovation Sandbox, Professional Sailor Jack Trigger, our Senior Data Scientist Roman Kastusik and our CEO, Eric Topham, discuss how they managed to teach AI how to sail. Throughout the episode, you will hear them discuss challenges, methodologies and implications of applying Machine Learning models to sailing boats as well as the next steps of the project.

2021 marks the second year of The Innovation Sandbox portal since it was launched and will see several new and continued projects taking place with new students and new partnerships. The Innovation Sandbox is a collaborative initiative at the confluence of industry-academia, allowing both academic types of research on real-world datasets and the transfer of cutting-edge machine learning research into industry application

In this episode, our CEO and Co-Founder, Dr Eric Topham and Dr Pedro Baiz from Imperial College discuss how The Innovation Sandbox Started. The focus has always been to create something innovative that could have a positive impact in real-life situation and scenarios and helping businesses and companies make the most of their data to achieve their goals. 

2021 marks the second year of The Innovation Sandbox portal since it was launched and will see several new and continued projects taking place with new students and new partnerships. The Innovation Sandbox is a collaborative initiative at the confluence of industry-academia, allowing both academic types of research on real-world datasets and the transfer of cutting-edge machine learning research into industry application

In this episode, our student researcher and scientist discuss their new project on the application of machine learning to predict Urinary Trait Infections (UTI) in care homes for early detection. They will guide us through challenges, goals and future implications of the project as well as their time scale and next steps.

2021 marks the second year of The Innovation Sandbox portal since it was launched and will see several new and continued projects taking place with new students and new partnerships. The Innovation Sandbox is a collaborative initiative at the confluence of industry-academia, allowing both academic types of research on real-world datasets and the transfer of cutting-edge machine learning research into industry application

In this episode, we will hear our scientists discuss their new project on the application of machine learning and natural language processing (NLP) for a leading e-commerce platform. They will guide us through challenges, goals and future implications of the projects as well as their time scale and next steps.