As specialists in helping organisations navigate new areas of tech, we wanted to share what we’ve learnt about how to successfully productionise ML projects. The practical points discussed here will benefit ML specialists and consultants, MLOps and project managers or software developers who are involved in productionising ML projects.
Machine learning and AI
Earlier this week we heard from Nigel Hooke—AI/Machine Learning Lead and Shahin Namin—Machine Learning Engineer at DiUS on their experience building commercial software applications powered by computer vision. Following their talk, the team took some really great questions from the audience. So, we thought we’d capture them here for anyone who either missed their talk, or wanted to refer back to them.
At DiUS, we always look for challenges in automation and testing when building solutions for our clients. Automated deployment and testing are key to any successful development process, and are particularly important for reproducible machine learning experiments. In this blog post I will explore how Amazon Personalize is helping to accelerate the machine learning lifecycle and where we think the challenges are for the important topic of automated deployment.
Rapid changes in customer behaviour requires businesses to adapt at an ever increasing pace. The recent changes to our work and personal life has forced entire nations to work remotely and do all non essential shopping online. With every challenge in business there is opportunity on the other side.