Kong announced the launch of the latest version of Kong AI Gateway, which introduces new features to provide the AI security and governance guardrails needed to make GenAI and Agentic AI production-ready.
We have become accustomed to so many different technologies in our lives today. Most share a common history — there was a democratizing force that brought it from an abstract, unattainable thing we only heard about to something we can no longer live without. Take the computer — for decades, computers were larger than refrigerators and were only accessible to the largest companies or nations. Today though, most of the world carries one in their pocket. Mass-marketed desktop computers balanced cost and operational restraints with measured capabilities tailored to the needs of an individual; they were relatively easy to use, and one did not need to know how they worked to get value from them. They provided immense value for its users.
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.
For businesses to remain competitive, they must invest time, money and resources into their AI capabilities. Luckily, with no-code AI, much like the desktop PC, there are varying levels of investment accessible to all instead of just those that can leap the historical barriers to entry. There is no need to establish a data-science practice, and there is no need to learn complex technologies.
To reap value, you need data and a system of automation; no-code AI brings the automation, and most organizations are already overflowing with data. A 2020 Salesforce report, which surveyed 100 global IT and engineering leaders, found that company departments utilizing workflow automation reduced time spent on manual processes by 66% and reduced cost by 46% compared to year-over-year outcomes. By streamlining manual processes through no-code AI models, businesses can scale faster while reducing the consumption of human time and increasing the accuracy of predictions.
Here are three key ways no-code AI is democratizing the technology:
1. No-Code AI Removes the Need for A High Degree of Training
It is no secret that a labor shortage exists for highly technical and skilled employees. Companies across all industries have difficulty finding and retaining qualified data scientists who can develop and create AI/ML models. This skills gap isn't going away anytime soon. No-code AI workflows empower citizen data scientists, including business analysts and other subject matter experts, to create, train and deploy powerful machine learning models. Another benefit of no-code AI is the relatively flat learning curve, allowing it to be quickly taught, replicated, and scaled across departments. This enables employers to combat the skills gap within their workforce by incorporating no-code tools training into employee development and onboarding.
2. Quicker Model Creation and Faster ROI
For companies lucky enough to have a data scientist on their staff, no-code AI enables them to work faster and more efficiently than before. No-code AI provides data scientists with automation and reusability that allows quick delivery of prototypes and iterative improvements using model explainability, revealing areas of model weakness. By employing no-code AI, data scientists can easily pinpoint problem areas within models, allowing managers to address any issues before they manifest into greater dilemmas quickly. Optimizing these processes frees up valuable bandwidth for data scientists to focus on more specialized research and projects within the company.
3. Better Explainability + Bias Detection Are Baked In
The Silicon Valley ethos of “move fast and break things” is gone. After years of eroding trust by big tech, consumers and businesses want to ensure the tech they invest in follows the “first do no harm” principle. Because no-code AI is created with the goal of enabling non-data scientists to harness the power of AI, it needs to be embedded with a higher level of explainability and bias detection to reduce the risk of human error.
No-code AI has proven to be a simple yet powerful solution aiming to bring AI to the masses. By democratizing AI with improved accessibility, businesses, regardless of size, will be able to compete more aggressively with AI in their toolbox.
Industry News
Traefik Labs announced significant enhancements to its AI Gateway platform along with new developer tools designed to streamline enterprise AI adoption and API development.
Zencoder released its next-generation AI coding and unit testing agents, designed to accelerate software development for professional engineers.
Windsurf (formerly Codeium) and Netlify announced a new technology partnership that brings seamless, one-click deployment directly into the developer's integrated development environment (IDE.)
The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, is making significant updates to its certification offerings.
The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, announced the Golden Kubestronaut program, a distinguished recognition for professionals who have demonstrated the highest level of expertise in Kubernetes, cloud native technologies, and Linux administration.
Red Hat announced new capabilities and enhancements for Red Hat Developer Hub, Red Hat’s enterprise-grade internal developer portal based on the Backstage project.
Platform9 announced that Private Cloud Director Community Edition is generally available.
Sonatype expanded support for software development in Rust via the Cargo registry to the entire Sonatype product suite.
CloudBolt Software announced its acquisition of StormForge, a provider of machine learning-powered Kubernetes resource optimization.
Mirantis announced the k0rdent Application Catalog – with 19 validated infrastructure and software integrations that empower platform engineers to accelerate the delivery of cloud-native and AI workloads wherever the\y need to be deployed.
Traefik Labs announced its Kubernetes-native API Management product suite is now available on the Oracle Cloud Marketplace.
webAI and MacStadium(link is external) announced a strategic partnership that will revolutionize the deployment of large-scale artificial intelligence models using Apple's cutting-edge silicon technology.
Development work on the Linux kernel — the core software that underpins the open source Linux operating system — has a new infrastructure partner in Akamai. The company's cloud computing service and content delivery network (CDN) will support kernel.org, the main distribution system for Linux kernel source code and the primary coordination vehicle for its global developer network.