As hard as it is for data scientists to tag data and develop accurate machine learning models, managing models in production can be even more daunting. Recognising model drift, retraining models with updating data sets, improving performance, and maintaining the underlying technology platforms are all important data science practices. Without these disciplines, models can produce erroneous results that significantly impact business. Developing production-ready models is no easy feat. According to one machine learning study, 55% of companies had not deployed models into production, and 40% or more require more than 30 days to deploy one model.

As hard as it is for data scientists to tag data and develop accurate machine learning models, managing models in production can be even more daunting. Recognizing model drift, retraining models with updating data sets, improving performance, and maintaining the underlying technology platforms are all important data science practices. Without these disciplines, models can produce erroneous results that significantly impact business. Developing production-ready models is no easy feat. According to one machine learning study, 55 percent of companies had not deployed models into production, and 40 percent or more require more than 30 days to deploy one model.

While the importance of the cloud is obvious to anyone, the increasing importance of the edge is often overlooked. As digitization and the Internet of Things are leading to an exponential growth in the number of devices, the amount of data that is being generated by sensors in devices such as self-driving-cars, mobile endpoints and people tracking systems for retail is astronomical. Analyzing and turning that data into immediate actions is key to success in the era of digitization. The cloud enables massive data storage and processing, but it does not always lend itself to real time processing and immediate actions. Latency and the sheer amount of data to be transmitted are much less of a factor for the edge compared to the data center.