JFrog announced a new machine learning (ML) lifecycle integration between JFrog Artifactory and MLflow, an open source software platform originally developed by Databricks.
A recent MIT/BCG study revealed that 84% surveyed feel AI is critical to obtain or sustain competitive advantage, and three out of four surveyed believe that Machine Learning provides an opportunity to enter new businesses and that AI will be the basis for new entrants into their industry. Which shouldn't come as a surprise to anyone, seeing as how advances in GPU/TPU technology, and the development of new platforms and frameworks have enabled an explosion in AI and Machine Learning, while new platforms from Amazon, Microsoft and others have put pre-built frameworks firmly in the grasp of developers. Despite all this movement, however, we are still definitely very early in the transition to using AI to transform software development — commonly referred to as Software 2.0, or AIOps.
Tesla is one shining example that emphasizes how early we are, and just how much expertise is required in an organization in order for the enterprise to gain the level of maturity necessary to take on this advanced, yet still esoteric, technology. Tesla uses computer vision, and other Machine Learning algorithms, to enable their vehicles to make literally thousands of decisions a millisecond. Most companies don't have anywhere near the comparable expertise in Artificial Intelligence and/or Machine Learning to take on this level of complexity on their own. But we remain optimistic, since Tesla's success thus far does inform what's possible in the near future.
The difficulty inherent in the transformation of DevOps to AIOps is that the two methodologies are not even close to being the same thing. Algorithmia, a company intent on "building the future of Machine Learning infrastructure," is one other organization that has already developed a flagship DevOps platform for AI. This tweet from Diego Oppenheimer, CEO/founder of Algorithmia, (quoting Mike Anderson, also of Algorithmia) illustrates what I mean when I say DevOps and AIOps are not one and the same: "Expecting your engineering and DevOps teams to deploy ML models well is like showing up to Seaworld with a giraffe, since they are already handling large mammals."
The low-code Lego models may be faster, but that doesn't mean they are optimized or efficient when you piece all the Legos together into a full-blown application. Though over time it's possible these components will improve. Some of the advantages of this approach can also be achieved (but perhaps without the continuous improvement of evaluating the quality of the code) through Reusable Component Libraries.
Many companies that may be eager to start down on the AI path will necessarily be relying on those familiar platform providers that are immediately available to them to improve/optimize code — such as the Microsoft Intellicode. We've also seen Apple launch SwiftUI, CreateML, and Reality Composer — all products aimed at reducing the coding effort as well as a significant investment in Swift (a far more efficient and declarative syntax that intrinsically requires less code) and the underlying ML and AR frameworks to pull it off. But like the Microsoft example, this is being led by the platform providers.
Industry News
Copado announced the general availability of Test Copilot, the AI-powered test creation assistant.
SmartBear has added no-code test automation powered by GenAI to its Zephyr Scale, the solution that delivers scalable, performant test management inside Jira.
Opsera announced that two new patents have been issued for its Unified DevOps Platform, now totaling nine patents issued for the cloud-native DevOps Platform.
mabl announced the addition of mobile application testing to its platform.
Spectro Cloud announced the achievement of a new Amazon Web Services (AWS) Competency designation.
GitLab announced the general availability of GitLab Duo Chat.
SmartBear announced a new version of its API design and documentation tool, SwaggerHub, integrating Stoplight’s API open source tools.
Red Hat announced updates to Red Hat Trusted Software Supply Chain.
Tricentis announced the latest update to the company’s AI offerings with the launch of Tricentis Copilot, a suite of solutions leveraging generative AI to enhance productivity throughout the entire testing lifecycle.
CIQ launched fully supported, upstream stable kernels for Rocky Linux via the CIQ Enterprise Linux Platform, providing enhanced performance, hardware compatibility and security.
Redgate launched an enterprise version of its database monitoring tool, providing a range of new features to address the challenges of scale and complexity faced by larger organizations.
Snyk announced the expansion of its current partnership with Google Cloud to advance secure code generated by Google Cloud’s generative-AI-powered collaborator service, Gemini Code Assist.
Kong announced the commercial availability of Kong Konnect Dedicated Cloud Gateways on Amazon Web Services (AWS).
Pegasystems announced the general availability of Pega Infinity ’24.1™.