Tag: DevOps

Futurum’s Steven Dickens provides his take on the latest announcements this week from IBM’s mainframe team. With the IBM Z team bringing to market the capability for customers to develop and test z/OS workloads to the IBM Cloud, Dickens examines the wider competitive landscape and the implications for IBM’s overall cloud growth.
Futurum Research Principal Analyst Daniel Newman and Senior Analyst Steven Dickens provide their take on Splunk’s Q2 earnings announcement. The company is shuffling the executive deck, making strategic investments to bolster its security portfolio and also transition to a recurring revenue based on workload pricing, rather than data ingestion and seems to be executing on all fronts given the strong numbers for Q2.
Futurum’s Steven Dickens gives his insight on the new DigitalOcean Managed MongoDB database as a service offering, focused on both developers and SMBs, as well as a look at what’s ahead for the industry as a whole, with the stakes being high for cloud providers.
If we don’t understand how machine learning works, how can we trust it? Increasing model transparency creates risks as well as rewards.
Microsoft Research AI ethics checklist is a set of published principles for designing ethics checklists that can be readily operationalized in AI DevOps processes. We commend Microsoft Research’s recent effort to catalyze consensus within the practitioner community for the purpose of developing clear principles for designing operationalizable AI ethics checklists. Though the researchers don’t publish a one-size-fits-all operationalizable AI ethics checklist, they provide a useful discussion of the scenarios within which checklists can be helpful, and also within which they can be counterproductive or irrelevant to their intended users. This is important as the abstract nature of AI ethics principles makes them difficult for practitioners to operationalize. As befits the scope of this topic, AI applications and tool vendors are still trying to bring their ethics-assurance frameworks into coherent shape. Everybody—even the supposed experts—are groping for a consensus approach and practical tools to make ethics assurance a core component of AI DevOps governance.
Algorithmia integrates AI model governance with GitOps, integrating ML and code development into DevOps workflows that use Git as a source-code repository. With this announcement, Algorithmia has made it easier to use GitHub to break down the silos that traditionally have kept ML developers and application coders from integrating tightly within today’s continuous DevOps workflows.
Launchable has emerged from stealth mode to introduce its AI-driven software test automation solution. This is exciting news for the DevOps community, as key industry figures—most notably, the Jenkins CI/CD automation server’s creator—have essentially validated that AI-driven test automation is coming big time into every software development shop. In a CI/CD context, Launchable’s adaptive AI can drive automated testing of source code changes upon check-in as well as notification of development and operations personnel when the tests fail. It can ensure that developers never have to wait more than a few minutes for feedback on their latest code changes. It can also help testers to keep pace with the growing volume, velocity, and variety of code changes, so that the most relevant changes can be tested 24x7. The challenge for Launchable is how quickly the company can gain traction in the developer community before incumbent startups in this promising niche solidify their first-mover advantage. Here are thoughts on how the company should move forward so as to quickly take advantage of this opportunity.
CES is not an enterprise-oriented tech event. It hosts a wide range of exhibitors and features a fair number of products and technologies. Chief among these versatile technologies is artificial intelligence (AI), which is in abundance at CES 2020.

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