Lecture 4

Today’s availability and low cost of digital technologies is fuelling the change of linear supply chains into always-on dynamic integrated networks, which are characterised by a continuous flow of information and analytics, and the merging of the physical and the digital. The key phrase here is ‘always on’, because for any company – especially manufacturers – there is no time so costly as machine downtime. Unplanned downtime can cost industrial manufacturers billions of dollars annually. Nearly half of such incidents are caused by equipment failure. Traditionally, such failure has been addressed retrospectively, through reactive maintenance programmes. 

SMBs was just beginning. As a pioneer in the SaaS industry, Salesforce provided businesses with a platform that constantly monitored and recorded employee actions and tracked KPIs. The vast amount of data it captured and its robust reporting capabilities allowed business owners to analyze performance metrics for individuals, teams and companies.

Today, nearly all business applications, including CRMs, accounting software and human resource information systems (HRIS), record real-time actions and changes. These platforms generate massive volumes of data that seamlessly integrate with analytics tools. This integration provides SMBs with detailed insights to inform cost reductions, improve profitability and identify inefficiencies. We’ll look at current trends affecting SMB data analytics and share how businesses use data analytics today.

Deep Learning is now the most popular technique for solving any Computer Vision task— from image classification and segmentation to 3D scene reconstruction or neural rendering. But how do you know if a deep model is performing well? We can use “accuracy” as an evaluation metric, right? So, what does "accuracy" really tell us? It tells us how many correct predictions a model will make when given 100 samples. Yet, that is not enough information to analyze a model's performance. What if the prediction task consists of 5 different classes of samples, and the model constantly makes wrong predictions on one of these classes, e.g., class-4? The model might seem to have an accuracy of 90% if the test set contains an imbalanced number of samples (i.e., samples from class-4 might be few), but still, it is not a good performer.

This is where confusion matrices come in.