Capturing big data is easy. What's difficult is to corral, tag, govern, and utilize it. NetApp, a hybrid cloud provider, sees cloud automation as a practice that enables IT, developers, and teams to develop, modify, and disassemble resources automatically on the cloud. Cloud computing provides services whenever it is required. Yet, you need support to utilize these resources to further test, identify, and take them down when the requirement is no longer needed. Completing the process requires a lot of manual effort and is time-consuming. This is when cloud automation intervenes.
All the sessions from Transform 2021 are available on-demand now. Dremio today launched a cloud service that creates a data lake based on an in-memory SQL engine that launches queries against data stored in an object-based storage system. The goal is to make it easier for organizations to take advantage of the data lake, dubbed Dremio Cloud, without having to employ an internal IT team to manage it, said Tomer Shiran, chief product officer for Dremio. An organization can now start accessing Dremio Cloud in as little as five minutes, he said. Based on Dremio's existing SQL Lakehouse platform, the Dremio Cloud service runs on the Amazon Web Services (AWS) public cloud.
Wind River today revealed a waterfall of new features available designed to automate and accelerate DevSecOps and other "pipelines" across the lifecycle of intelligent systems. The latest release of their platform is focused on transformational automation technologies, including a customizable automation engine, digital feedback loop, enhanced security, and analytics with machine learning capabilities. The announcement also included industry-proven technologies from ecosystem partners to the Wind River Studio Marketplace, which makes solutions available that are developed and delivered on the Wind River Studio "cloud-native platform for the development, deployment, operations, and servicing of mission-critical intelligent systems from devices to cloud." The company claims the platform "enables dramatic improvements in productivity, agility, and time-to-market, with seamless technology integration that includes far edge cloud compute, data analytics, security, 5G, and AI/ML." "The next generation of cloud-connected intelligent systems require the right software infrastructure to securely capture and process real-time machine data with digital feedback from a multitude of embedded systems, enabling advanced automated and autonomous scenarios," said Kevin Dallas, president, and CEO, Wind River.
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Machine learning (ML) techniques are the fundamental building block for AI services. In the past, they have been out of reach of most enterprise budgets due to their costly hardware requirements. The ability of public cloud providers like Microsoft Azure to offer on-demand, low-cost computing power with benefits such as scalability, efficiency, and adaptability make this technology affordable today. With more and more enterprises transferring their workloads to the cloud to enable new business models as well as cost reduction, privacy and security become key concerns as the confidentiality and integrity of code and data are subject to trust the cloud service provider (CSP). However, the CSP is not the only party that needs to be trusted.
At its annual MongoDB.Live event this week, MongoDB is unveiling the next major release – version 5.0 – of its eponymous database. To some extent, the highlights for MongoDB 5.0 are not surprising, as there's a greater focus on productivity for its core constituency of developers. But the new release also expands the umbrella of data types with new time series support, followed by features that would be considered enterprise-friendly. Underscoring this is that MongoDB, and a rapidly growing cross section of its customer base, are going cloud-first. As of the latest quarter, Q1 FY 2022 which was reported back in June, the Atlas managed cloud database-as-a-service (DBaaS) now accounts for 51% of overall revenues.
Google Cloud continued to expand upon its big data, analytics and machine learning strengths this year, with new products and services ranging from an artificial intelligence platform to help developers more quickly build and deploy ML models to a database migration service for customers to more easily migrate their data to its cloud platform. The No. 3 cloud computing provider also is providing -- in preview for now -- access to the daily top 25 Google Search terms in BigQuery, its analytics data warehouse. And continuing with its focus on delivering industry-specific offerings to enterprises, Google Cloud released a purpose-built manufacturing solution to improve production quality, a financial services solution for licensed market data discovery, access and analytics on Google Cloud, and its new Cloud Healthcare Consent Management API. Here's a look at those offerings and others that have made CRN's list of the hottest Google Cloud tools so far this year.
But these AI outcomes don't come cheap: the AI model must be "trained" by recreating countless permutations of those outcomes, and that training eats up enormous amounts of computer power on mammoth GPUs. So they naturally turn to using cloud services like AWS, Microsoft Azure, and Google Cloud to train their AI models. Those wanting compute power can establish an Akash account and bid for compute power. The one providing computer power on the Akash Network is called a Provider. The point is this: By buying more AKT than needed to fund a deployment on Akash Network, the Tenant can recover some of the deployment costs through (1) staking rewards and (2) appreciation of the value of AKT (assuming that value increases).
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Google has been working hard to help data researchers or AIs with easy-to-use and accessible tools. TensorFlow and Google Colab would be some of the popular offerings from Google. To make matters easier for data scientists and machine learners to go further with tools such as Cloud AI, Cloud AutoML, and BigQueryML, the Google Cloud Platform.