Why machine learning projects fail and how to make them succeedIn this blog, Alexander explains the challenges that come with Machine Learning projects, outlining the reasons why many of them fail, and highlighting the steps that organisations can take to ensure they succeed.
CleverHealth Ecosystem: Harnessing artificial intelligenceThere are great expectations from artificial intelligence solutions for brain imaging with CleverHealth Network, which is aiming for it to become a multi-million Euro business.
Guiding clients on their artificial intelligence journeys
In helping clients benefit from the power of AI, we start with an idea, identify the critical data and rapidly turn that idea into a minimum viable product. We then help scale the solution, and continuously manage and improve it. In doing so, we leverage cloud-based and on-premise AI accelerator platforms, including access to high performance computing. We also help clients transition to an industrialised and mature AI operating model.
We work with clients locally, while bringing the strength of our global insights and end-to-end skills. We also provide a rich global and local ecosystem of AI specialty and academic partners. Our data scientists, together with our domain experts, identify realistic use cases. In addition, our method of experimenting, learning and applying human psychology and empathy enables us to deliver pragmatic and responsible AI innovation and transformative experiences.
Using machine learning to predict cracks in steel
Steel manufacturer Uddeholm turned to CGI to increase steel quality and reduce unnecessary waste due to cracks in steel. CGI developed a solution for predicting steel cracks that uses big data and IoT to capture relevant data, along with machine learning and advanced analytics to generate insights. With the solution, cracks can be predicted with 70% accuracy.
Combining responsible AI with human creativity to propel you to new horizons
- AI spark session - Half to one day workshop to demystify AI, with two goals—inspire what’s possible with AI and envision AI use cases
- AI data science: Use case validation - 1-2 weeks of data scientist-led use case validation
- AI MVP / POV / POC - 4-6 week project typically involving a design lab and rapid prototyping and/or an MVP sprint
- Production and scale - Leverage AI platforms to scale prototypes / MVPs into production
- Enterprise AI operating model: Assess AI operations and offer recommendations for an industrialized operating model
- Continuous improvement - Leverage production platform for continuous model improvement and life cycle management