Businesses' growing desire for more and more public cloud services is showing no signs of slowing down, according to the latest figures from Gartner. "Cloud is the powerhouse that drives today's digital organizations," said Sid Nag, research vice president at Gartner. The analyst house predicts businesses will continue to break up larger, monolithic applications into composable parts for DevOps processes. "Cloud native capabilities such as containerisation, database platform-as-a-service (dbPaaS) and AI/machine learning contain richer features than commoditised compute such as IaaS or network-as-a-service," said Gartner's Nag. The news comes after Gartner predicted earlier this month that global IT spending is set to reach a total of $4.4tn this year despite rising inflation, geopolitical disruption and ongoing talent shortages.

At the AWS re:Invent conference, Amazon Web Services (AWS) added a bevy of tools to its portfolio intended to accelerate the pace of application development while simultaneously simplifying DevOps processes. AWS development tools unveiled this week include AWS Amplify Studio, a visual development environment that allows developers to create web application user interfaces (UIs) with minimal coding. Ken Exner, head of product for developer tools at AWS, said as an extension of an existing AWS Amplify Studio tool, this latest addition allows developers to customize application UIs at a higher level of abstraction using a library of components while enabling them to drop down to a lower level of coding to customize their application further whenever required. After the UI is designed, AWS Amplify Studio automatically generates the associated JavaScript or TypeScript code for the developer. In general, AWS is committed to improving developer productivity by providing, for example, automated reasoning tools that automate processes without locking devs into a layer of abstraction that, ultimately, may create a wall that blocks them from meeting customization requirements, he said.

Developing and deploying AI-powered systems and applications is a complex business, especially in our extended remote reality. You're likely facing an uphill climb and let's face it, huge risks. The way to clear the obstacles, lower the risks, and raise the value you deliver hinges on one essential element: implementing DevSecOps to protect your process and your assets. We're operating in a different world now where unity among development (Dev), security (Sec), and operations (Ops) has never been more essential. Compounded by pressure related to the fast need to convert many of our office infrastructure to meet the needs of our remote reality during the COVID-19 pandemic, the market for DevSecOps is projected to grow from 32% to 34% mid-decade.[i]

Algorithmia, in the latest update to the enterprise edition of its namesake machine learning operations (MLOps) platform, is enabling software development lifecycle practices to be applied to the building of algorithms by making it possible to debug them using desktop tools that are widely employed. The enterprise edition of Algorithmia will enable users to write and run local tests for algorithms as shared local data files. Desktop developer tools that can now integrate with that process include PyCharm, Jupyter Notebooks, R Shiny, Android, iOS, Cloudinary, Datarobot and H2O.AI. In addition, Algorithmia MLOps has extended its support for the latest graphical processor units (GPUs) available on Amazon Web Services (AWS) and Microsoft Azure. Finally, the company has added support for AWS C2S, a private cloud for intelligence services, and AWS GovCloud.

"In the era of big data, insights collected from cloud services running at the scale of Azure quickly exceed the attention span of humans. It's critical to identify the right steps to maintain the highest possible quality of service based on the large volume of data collected. In applying this to Azure, we envision infusing AI into our cloud platform and DevOps process, becoming AIOps, to enable the Azure platform to become more self-adaptive, resilient, and efficient. AIOps will also support our engineers to take the right actions more effectively and in a timely manner to continue improving service quality and delighting our customers and partners. This post continues our Advancing Reliability series highlighting initiatives underway to keep improving the reliability of the Azure platform. The post that follows was written by Jian Zhang, our Program Manager overseeing these efforts, as she shares our vision for AIOps, and highlights areas of this AI infusion that are already a reality as part of our end-to-end cloud service management."--Mark

"In the era of big data, insights collected from cloud services running at the scale of Azure quickly exceed the attention span of humans. It's critical to identify the right steps to maintain the highest possible quality of service based on the large volume of data collected. In applying this to Azure, we envision infusing AI into our cloud platform and DevOps process, becoming AIOps, to enable the Azure platform to become more self-adaptive, resilient, and efficient. AIOps will also support our engineers to take the right actions more effectively and in a timely manner to continue improving service quality and delighting our customers and partners. This post continues our Advancing Reliability series highlighting initiatives underway to keep improving the reliability of the Azure platform. The post that follows was written by Jian Zhang, our Program Manager overseeing these efforts, as she shares our vision for AIOps, and highlights areas of this AI infusion that are already a reality as part of our end-to-end cloud service management."--Mark

The automation wave has overtaken IT departments everywhere making DevOps a critical piece of infrastructure technology. DevOps breeds efficiency through automating software delivery and allowing companies to push software to market faster while releasing a more reliable product. What is next for DevOps? We need to look no further than artificial intelligence and machine learning. Most organizations quickly realize the promise of AI and machine learning, but often fail to understand how they can properly harness them to improve their systems.

However, according to a study by Appian last year, 91 percent of respondents believe they need to fix problems rather quickly than thoroughly, as they need to focus on updating their business operations. The speed of updates and customizations using DevOps measure is measured in days rather than months or years. Frequently, IT operations cannot keep up with this pace, and digitization will fail in the long term. The solution to many of the associated DevOps challenges lies in a different kind of digital transformation - based on machine learning and artificial intelligence (AI). Although it is often portrayed as a threat to the public, it offers companies an excellent opportunity to improve their productivity and security.

Ever heard of AI for IT management (AIOps)? It's a burgeoning ecosystem of platforms and products that enhance IT operations by leveraging AI to analyze data collected from various tools and devices. OpsRamp is one of the startups leading the charge in what Research and Markets anticipates will be a $14.3 billion segment by 2025. Raju Chekuri and Varma Kunaparaju cofounded the San Jose, California-based company in 2014 with the goal of developing "service-centric" offerings for enterprises, and their investors evidently believe they've achieved that. Today OpsRamp announced the closing of a $37.5 million investment led by Morgan Stanley Expansion Capital, with new investor Hewlett Packard Enterprise.

How do you benchmark the "evil" quotient in your AI app? That may sound like a facetious question, but let's ask ourselves what it means to apply such a word as "evil" to this or any other application. And, if "evil AI" is an outcome we should avoid, let's examine how to measure it so that we can certify its absence from our delivered work product. Obviously, this is purely a thought experiment on my part, but it came to mind in a serious context while I was perusing recent artificial intelligence industry news. Specifically, I noticed that MLPerf has recently announced the latest versions of its benchmarking suites for both AI inferencing and training.