When a business considers switching to a new MDM for their remote support, they primarily evaluate potential solutions from a technological perspective. The organization’s IT team will compare the MDM’s features vis-a-vis others in the industry, weigh over its specs, and determine how an implementation would work for their business. While this comprehensive technology review is important, the problem is that most businesses stop there.

When adding new Checks in Checkly a number of locations are available to check your endpoints from multiple locations around the world. For most use cases this is more than enough to ensure your resources are online. However, these locations are outside of your network and are unable to check on resources deployed more securely inside your private network.

The adoption of AI/ML in financial services is increasing as companies seek to drive more robust, data-driven decision processes as part of their digital transformation journey. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. But productionising machine learning at scale is challenging.

With more and more applications moving to the cloud, an increasing amount of telemetry data (logs, metrics, traces) is being collected, which can help improve application performance, operational efficiencies, and business KPIs. However, analyzing this data is extremely tedious and time consuming given the tremendous amounts of data being generated. Traditional methods of alerting and simple pattern matching (visual or simple searching etc) are not sufficient for IT Operations teams and SREs.

Modern business of any direction requires the introduction of IT technologies. Large companies can afford to hire a team of specialists who support customers and company employees in information technology. At the same time, specialists manage to implement new solutions in their platforms simultaneously. Small and medium businesses need help to afford such a solution.