Where Are We in the Evolution to Software 2.0?
April 09, 2020

Glenn Gruber
Anexinet

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.

Glenn Gruber is a Senior Digital Strategist at Anexinet
Share this

Industry News

August 06, 2020

Push Technology announced the launch of a new Kafka Adapter for their Diffusion Intelligent Data Mesh.

August 06, 2020

Appvia announced the launch of its Cost Prediction and Visibility tool, integrated within the latest version of its Kore platform.

August 06, 2020

LogiGear announced the newest addition to the TestArchitect™ family, TestArchitect Gondola.

August 05, 2020

Logz.io announced a partnership with HashiCorp, a provider in multi-cloud infrastructure automation software.

August 05, 2020

Digitate, a software venture of Tata Consultancy Services, announced the release of ignio™ AI.Assurance, an autonomous assurance product that enables enterprises to deliver better software faster, enhancing their business performance.

August 05, 2020

Harness acquired self-service Continuous Integration firm Drone.io, the creator of the open-source project Drone.

August 04, 2020

Aqua Security announced that its Cloud Native Security Platform is available through Red Hat® Marketplace, an open cloud marketplace that makes it easier to discover and access certified software for container-based environments across the hybrid cloud.

August 04, 2020

Threat Stack announced the availability of Threat Stack Container Security Monitoring for AWS Fargate.

August 04, 2020

OpenLogic by Perforce now provides an enterprise-class alternative to Oracle Java by offering OpenJDK distributions backed by OpenLogic support.

August 03, 2020

MuseDev launched on Github Marketplace the Early Access version of its code analysis platform, Muse, to help developers find and fix critical security, performance, and reliability bugs, efficiently, before they reach QA or production.

August 03, 2020

Styra announced Rego Policy Builder for the Styra Declarative Authorization Service (DAS).

August 03, 2020

Felicis Ventures has invested an additional $5M in Sourcegraph, bringing the total raised to over $46M, including a $23M Series B in March 2020 led by Craft Ventures.

July 30, 2020

New Relic delivered strategic updates to New Relic One.

July 30, 2020

IT Revolution announced the DevOps Enterprise Summit Las Vegas 2020 will be going virtual.

July 30, 2020

Adaptavist announced the acquisition of Go2Group, a US technology firm specializing in Agile and DevOps services and cloud solutions for the enterprise.