Pinpointing gaps within an organization and acquiring people with the skills to drive a digital revolution is difficult. And ongoing labor shortages, the impact of automation on job roles, and continuously evolving technology doesn't make it any easier ... What can be done to help organizations not just survive, but also thrive in this new, rapidly changing reality? ...
Vendor Forum
The low-code platform market offers enterprises a large selection of options with players of varying sizes and capabilities co-existing. However, the market's complexity is also one of the key reasons the technology has not realized its full potential. To find out what the future holds for this industry and the different scenarios that may unfold, read on ...
Looking at the world of emerging technologies, artificial intelligence (AI) appears to be the next technology on the cusp of breaking into the mainstream, thanks to the development and growing proliferation of no-code AI. A code-free technology that enables non-AI experts to implement and test their ideas without any need for a data scientist, no-code AI will prove to be a democratizing force within the AI industry enabling greater accessibility and use by businesses outside of the Fortune 100 ...
Open source security is increasingly in the headlines, with a staggering 650% rise in open source supply chain attacks last year. New forms of attack, like "dependency confusion" are hurting organizations with alarming regularity. Given how widespread open source is within enterprise tech, one insecure package can cause a ripple effect around the globe ...
As technical recruiters know, the developer landscape today looks significantly different from the landscape of a year or two ago. Developers, like other employees, are adapting to changing workplace trends and adopting different perspectives on what work-life balance means for them ...
In the mobile app development world, security often takes a backseat to developing features and delivering the app. In fact, the 2021 Verizon Mobile Security Index found that 45% of organizations sacrificed mobile security in order to “get the job done” ...
Kubernetes is a thriving open-source project delivering rapid innovation with releases three times a year. If using fully managed Kubernetes from public cloud providers, be prepared for Kubernetes service life cycles that are aggressive ...
As containers become the default choice for developing and distributing modern applications and Kubernetes (k8s) the de-facto platform for deploying, running, and scaling such applications, enterprises need to scale their Kubernetes environments rapidly to keep up. However, rapidly scaling Kubernetes environments can be challenging ...
DevOps mentality has moved from strictly the development domain into the Ops world. As a result, traditional Ops — a model rooted in manual processes and executed by siloed teams — is dead. Traditional Ops and IT teams were already experiencing rapid change, but the pandemic sped up the adoption of more decentralized operations. Today, Ops doesn't generally refer to a team, but a responsibility. This blog will contrast Ops in a traditional environment vs. a DevOps environment, challenges to be aware of when adopting a DevOps approach, and actionable steps for organizations looking to implement a DevOps culture ...
The first learning from big tech is: Most large companies — wanting to adopt AI — hire teams to build internal platforms for ML practitioners. But these data scientists or ML engineers are often not familiar with enterprise software engineering. Expecting them to learn is feasible but inefficient, which is where MLOps platforms come in ...
Machine learning operations (MLOps), combining ML and software engineering, are becoming more mainstream and intend to improve the quality and speed of delivering ML models to production. Business leaders are missing out on many opportunities without proper MLOps platforms in place. So, how can companies get started? ...
ML engineering can be defined as the technical systems and processes associated with the stages of the ML lifecycle (also referred to as MLOps cycle) from data preparation, modeling building, and production deployment and management. While ML engineering entails the provisioning, deployment, and management of infrastructure that enables model building, data labeling, and model inference, it can go much deeper than that ...
Corporations can spend millions to install effective cybersecurity infrastructure, but what they might fail to notice is that vulnerabilities could be hiding in plain sight in developer repositories. To make database connections, calls to APIs, and many other functions more convenient, developers will often hardcode various credentials, keys, and secrets into a configuration file, or sometimes directly into a function itself. While this practice makes it convenient for developers, it opens up a myriad of vulnerabilities and cybersecurity challenges ...
Infrastructure-as-code (IaC) is regularly used by DevOps teams. However, the increasing complexity of things like data center configurations, security requirements, and changing guidelines means IaC is poised for an overhaul ...
Kubernetes and the ecosystem of cloud native technologies unlock innovation for organizations and provide a means to achieve the goals of elasticity, agility, optimized resource utilization, reduced service costs and workload portability. Security and optimized resource utilization are high priorities for practitioners, reminding us that the cloud native space is maturing, and focus is moving from Day Zero to Day Two operations, according to the Kubernetes and Cloud Native Operations survey report from Canonical ...
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