We often get brought on board to help productise 'IoT' (that's Internet of Things, if you have somehow managed to escape the acronym) devices, and depending on the domain it's often a Linux based device. Something we commonly see is that developers who are entering the embedded Linux space from the server or desktop direction are carrying over patterns from there out of habit.
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.
Traditional approaches like SARIMA models often require manual data pre-processing steps (e.g. differencing to make the data stationary). In addition, these models are not allowed to add additional domain knowledge to improve precision. For solving these problems, Facebook researchers recently released FBProphet.
