Cosmonic announced the launch of Cosmonic Control, a control plane for managing distributed applications across any cloud, any Kubernetes, any edge, or on premise and self-hosted deployment.
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
Oracle announced the general availability of Oracle Exadata Database Service on Exascale Infrastructure on Oracle Database@Azure(link sends e-mail).
Perforce Software announced its acquisition of Snowtrack.
Mirantis and Gcore announced an agreement to facilitate the deployment of artificial intelligence (AI) workloads.
Amplitude announced the rollout of Session Replay Everywhere.
Oracle announced the availability of Java 24, the latest version of the programming language and development platform. Java 24 (Oracle JDK 24) delivers thousands of improvements to help developers maximize productivity and drive innovation. In addition, enhancements to the platform's performance, stability, and security help organizations accelerate their business growth ...
Tigera announced an integration with Mirantis, creators of k0rdent, a new multi-cluster Kubernetes management solution.
SAP announced “Joule for Developer” – new Joule AI co-pilot capabilities embedded directly within SAP Build.
SUSE® announced several new enhancements to its core suite of Linux solutions.
Progress is offering over 50 enterprise-grade UI components from Progress® KendoReact™, a React UI library for business application development, for free.
Opsera announced a new Leadership Dashboard capability within Opsera Unified Insights.
Cycloid announced the introduction of Components, a new management layer enabling a modular, structured approach to managing cloud resources within the Cycloid engineering platform.
ServiceNow unveiled the Yokohama platform release, including ServiceNow Studio which provides a unified workspace for rapid application development and governance.
Sonar announced the upcoming availability of SonarQube Advanced Security.
ScaleOut Software introduces generative AI and machine-learning (ML) powered enhancements to its ScaleOut Digital Twins™ cloud service and on-premises hosting platform with the release of Version 4.