How to Use Analytics to Detect Hidden Anomalies in Cloud Application Environments. When supporting some of the largest and most successful SaaS and digital enterprise companies worldwide, we at Tanzu VMware Observability get to learn from our customers on a regular basis. We see how they structure their operations, how they implement their monitoring and automation policies, and how they use smarter alerts to lower mean time to identify and mean time to automate.

In this paper, we talk about four common challenges to today's software delivery automation and discuss how you can overcome those challenges. Continuous delivery (CD) enables organizations to deliver quality software in record time, helping businesses meet rapidly growing and changing demands. As CD practices and tools have matured, more organizations and teams are beginning - or are well down the path of - their own journeys, using automation to speed up software delivery.

Are you looking to migrate from your on-premises data center to AWS - or to accelerate the process? Or would you like a simple and reliable way to manage your infrastructure and applications across all your environments? Puppet can help.

Businesses around the globe are adopting a work-from-home model to maintain business continuity. Remote work demands access to on-premises services and applications, and a virtual private network (VPN) serves as a conduit to bridge this gap. However, choosing the right VPN without proper technical expertise is quite challenging owing to the plethora of choices available in the market. Download this e-book to learn how to select the right VPN to maximize uptime of your business operations.

A majority of companies are moving towards a work-from-home model in an effort to reduce costs and improve operational agility. However, along with these advantages, a remote workforce brings up numerous security concerns. Download this e-book to learn how to secure your network and safely support a remote workforce.

From the smallest startups to the largest enterprises alike, organisations are using Artificial Intelligence and Machine Learning to make the best, fastest, most informed decisions to overcome their biggest business challenges. But with AI/ML complexity spanning infrastructure, operations, resources, modelling and compliance and security, while constantly innovating, many organizations are left unsure how to capture their data and get started on delivering AI technologies and methodologies.

Traditional development methods do not scale into the IoT sphere. Strong inter-dependencies and blurred boundaries among components in the edge device stack result in fragmentation, slow updates, security issues, increased cost, and reduced reliability of platforms. This reality places a major strain on IoT players who need to contend with varying cycles and priorities in the development stack, limiting their flexibility to innovate and introduce changes into their products, both on the hardware and software sides.

AIOps, i.e., artificial intelligence for IT operations, has become the latest strategy du jour in the IT operations management space to help address and better manage the growing complexity and extreme scale of modern IT environments. AIOps enables some unique and new capabilities on this front, though it is quite a bit more complicated than the panacea that it is made out to be. However, the underlying AI and machine learning (ML) concepts do help complement, supplement and, in particular cases, even supplant more traditional approaches to handling typical IT Ops scenarios at scale.