Organizations across the globe are beefing up their cybersecurity efforts to thwart cyberattacks, relying on various solutions such as firewalls and zero-trust network access (ZTNA). Most organizations overlook network analytics as a tool to protect digital infrastructure and assets. This article will help you understand what network analytics is and how it can be used to strengthen cybersecurity.

Global supply chain disruption costs the average large business $182 million a year – despite the deployment of more than 10 billion Internet of Things (IoT) devices worldwide, which are constantly adding data to already overflowing data stores. The problem is not a lack of data, which is why IoT is not the solution.

Productionizing machine learning models is complex, and MLOps (machine learning operations) practitioners have rightly looked to software engineering processes as a blueprint, seeking to adapt traditional development and IT operations (DevOps) thinking to the particular needs of ML. This has led to the emergence of an explosion of tools to streamline and automate various parts of the ML life cycle. Collectively, this vibrant, evolving and sometimes confusing set of tools constitutes the MLOps technology stack.

Machine learning and predictive analytics immediately capture the imagination as people try to figure out how to use data to grow their business. “What would we do differently if we knew what was coming?” is a common question in the world of data analytics. However, there are a number of challenges with this problem framing that often reduce the success of data projects. In this article, you will learn about some of those challenges and a better approach to leveraging data.