The former Forrester Research Director Chris Mines predicted in 2019 that the world of software development was set for some big changes in 2020. We had no idea that a year later, almost every development shop would be a remote development shop. It makes the curated list of "remote-friendly" companies on GitHub a nostalgic reminder of a simpler, pre-pandemic time. Most developers adjusted well to the changes in 2020, certainly compared to other professions. Working hours increased and work weeks lengthened, but our digital world didn't come crashing down like other sectors of the global economy.
Ever since IBM unveiled Cloud Pak for Data as a cloud-native integrated set of analytics and AI platform, we've been wondering when IBM would take the next step and announce a full-blown managed cloud service. It's now starting to happen as IBM is rolling out IBM Cloud Pak for Data as a Service. Roll back the tape to last spring when we reviewed IBM Cloud Satellite; we noted that IBM's primary cloud message has been about multi-cloud, or at least cloud-agnostic. Propelled by Red Hat OpenShift, IBM carved out such a strategy for this managed Kubernetes environment where you could deploy open source software yourself on the hardware or public cloud of your choice or choose IBM to run a managed OpenShift service for you in the IBM Cloud. That is now getting repeated with Cloud Pak for Data.
Google Cloud Platform provides us with a wealth of resources to support data science, deep learning, and AI projects. Now all we need to care about is how to design and train models, and the platform manages the rest tasks. In current pandemic environment, the entire process of an AI project from design, coding to deployment, can be done remotely on the Cloud Platform. IMPORTANT: If you get the following notification when you create a VM that contains GPUs. You need to increase your GPU quota.
Red Box, a leading platform for voice, announces an extension of its relationship with Microsoft aligned to the launch of Conversa, Red Box's enterprise voice platform. Red Box is already a Preferred Telephony Partner for conversation intelligence, part of Microsoft Dynamics 365 Sales and Customer Service. This latest development in the relationship delivers a unique capture layer for enterprise voice. It combines the power of Conversa audio processing in Microsoft Azure and Microsoft AI, with seamless support of both cloud and premise-based telephony aligned with frictionless zero touch implementations. The on-premise self-install capability provided by Conversa, and powered by Azure Cloud, will simplify the delivery of'AI-Ready', real-time voice capture for those organizations that struggle to gain access to audio data.
To help its service technicians more efficiently repair and maintain its models, Mercedes-Benz USA is outfitting all of its authorized American dealerships with HoloLens 2 headsets. The devices are equipped with Microsoft Dynamics 365 Remote Assist, a mixed reality app that that lets users collaborate during hands-free video calls from their own computers. Organizations have long known the importance of business resiliency, but becoming resilient requires time and preparation, and the pandemic has forced many organizations to evolve at a pace few could have imagined. To recover and thrive within this new context presents new challenges. That is why we are partnering with customers to support faster adoption of digital capabilities.
Across a range of industries, and specifically in the industrial automation vertical, there is broad agreement that the deployment of modern computing resources with cloud native models of software lifecycle management will become ever more pervasive. Placing virtualized computing resources nearer to where multiple streams of data are created is well established. It is the path to address system latency, privacy, cost and resiliency challenges that a pure cloud computing approach cannot address. This paradigm shift was initiated at Cisco Systems around 2010, under the label "fog computing" and progressively morphed into what is now known as "edge computing". The requirements of mission critical industrial systems That said, the full potential of this transformation in both computing and data analytics is far from being realized.
International Business Machines (IBM) - Get Report and Advanced Micro Devices (AMD) - Get Report said they began a development program focused on cybersecurity and artificial intelligence. The development agreement will build on "open-source software, open standards, and open system architectures to drive confidential computing in hybrid cloud environments," the companies said in a statement. The agreement also will "support a broad range of accelerators across high-performance computing and enterprise critical capabilities, such as virtualization and encryption," they said. AMD, Santa Clara, Calif., is one of the world's biggest chipmakers and is thriving. IBM, the storied Armonk, N.Y., technology services company, has struggled to regain the glory of its past, when it led the computer-making industry.
In the first blog in this series, we discussed how data availability, data access, and insight access have evolved over time, and what Google Cloud is doing today to help customers democratize the production of insights across organizational personas. In this blog we'll discuss why artificial intelligence (AI) and machine learning (ML) are critical to generating insights in today's world of big data, as well as what Google Cloud is doing to expand access to this powerful method of analysis. A report by McKinsey highlights the stakes at play: by 2030, companies that fully absorb AI could double their cash flow, while companies that don't could see a 20% decline. ML and AI have traditionally been seen as the domain of experts and specialists with PhDs, so it's no surprise that many business leaders frame their ML goals around HR challenges: creating new departments, hiring new employees, developing retaining programs for the existing workforce, and so on. But this isn't the way it has to be.
IBM Corp. said today it's hoping to provide a standardized solution for developers to create and deploy machine learning models in production and make them portable to any cloud platform. To do so, it said it's open-sourcing the Kubeflow machine learning platform on Tekton, a continuous integration/continuous development platform developed by Google LLC. It's popular with developers who use Kubernetes to manage containerized applications, which can run unchanged across many computing environments. IBM said it created Kubeflow Pipelines on Tekton in response to the need for a more reliable solution for deploying, monitoring and governing machine learning models in production on any cloud platform. That's important, IBM says, because hybrid cloud models are rapidly becoming the norm for many enterprises that want to take advantage of the benefits of running their most critical business applications across distributed computing environments.
AVEVA, a global leader in engineering and industrial software, announced that it will be extending its long-standing strategic collaboration with Microsoft to focus on accelerating digital transformation in the industrial sector. AVEVA will help maximize the value that customers can derive from the integration of AVEVA's portfolio with Microsoft cloud services and especially Microsoft Azure (infrastructure, data and AI services), helping them achieve implementations quicker, connect teams more readily and drive growth opportunities throughout their integrated portfolio. AVEVA's key focus areas will revolve around cloud as well as transforming the workforce (connected worker), and building a common Asset Strategy (Asset Performance). Working with Microsoft, AVEVA will continue to focus on three key areas, already proven with customers including Total, Veolia and SCG Chemicals - platform integration, a multi-solution engagement approach, and a shared go-to-market strategy. The platform integration approach can help generate new ways to increase business value for customers.