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Learn how Locus uses advanced synthetic monitoring from Checkly to ensure the reliability of its Dispatch Management Platform Locus.sh is a leading-edge technology company solving one of the most challenging global supply chain problems: last-mile logistics. Locus' order-to-delivery dispatch management platform helps enterprises transform their last-mile logistics operations into growth centers.
Testing applications behind a login flow is cumbersome. And it gets even worse when there’s two-factor authentication (2FA) involved. Many people work around this problem by disabling it or implementing wild hacks. Automating a 2FA-based login flow is just too hard! I thought that for a long time, too. But I must admit — I was wrong.
As an observability provider, we are always confronted with our clients’ goal for faster resolution of problems and better overall performance of their systems. By working on large-scale projects at Logz.io, I see the same main challenge coming up for all: extracting valuable insights from huge volumes of data generated by modern systems and applications.
For those outside the grocery industry, direct store delivery (DSD) is a distribution model used by manufacturers/CPGs that entails delivering products directly to retail store shelves. This model is extremely conducive for perishable food categories. During your typical grocery shopping, you may notice that store employees these days do relatively little replenishment in the aisles.
Your employees and customers are constantly on the move and rely on their mobile devices to stay connected and productive. On-demand remote support is crucial when technical issues become a roadblock in their day. With mobile device remote support, your IT team can troubleshoot problems quickly and efficiently from a tablet, computer, or smartphone.
MLOps is the short term for machine learning operations and it represents a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. It accomplishes the deployment and maintenance of models reliably and efficiently for production, at a large scale. MLOps is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from data gathering to governance and monitoring.
