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The Data Science Virtual Machine (DSVM) is a powerful data science development environment where you can perform data exploration and modeling tasks. The environment comes already built and bundled with several popular data analytics and data science tools that make it easy to get started without spending 20 min to 1 hour deploying a suitable infrastructure.
A new learning experience for R developers
Also, we are delighted to announcesome exciting new updates whichmakes R a 1st Class developer experience forlearners on DSVMs and on Learn Sandboxes on MS Learn. Starting from April 2022, the DSVM offering has been enriched by DSVM for Windows 2019 v. 22.04.21, DSVM for Ubuntu 20.04 and Ubuntu 18.04 v. 22.04.27, which provide an updated R environment including the following R libraries: Cluster, DevtoolsFactoextra, GlueHere, Ottr, Paletteer, Patchwork, Plotly, Rmd2jupyter, Scales, Statip, Summarytools, Tidyverse, Tidymodels and Testthat.
Getting started with R on DSVMs : a guided tutorial
But how you can start using R on DSVMs in your course or lab to perform data science tasks? Let’s go through all the steps you’ll need to create a DSVM on Azure and run a R Jupyter notebook.
2. Sign in to the Azure Portal and search for “data science virtual machine”. Choose one of the resulting offerings by clicking on it. For this tutorial we will use Data Science Virtual Machine – Ubuntu 20.04.
3. Choose a resource group and the name of the VM you want to create as well as the Azure subscription on which the machine will be billed. Select the datacenter region closest to your physical location and, for quicker set up, select “Password” as authentication type. Then specify the username and password you’ll use to login into your virtual machine.
Click on Review + create and wait until the deploy is succesfully completed.
4. There are different ways to access your DSVM. One of these is Jupyter Hub,a multiuser Jupyter server. To connect, open a web browser from your local machine and navigate to https://your-vm-ip:8000, replacing “your-vm-ip” with the IP address you can find in the overview section of your resource.
5. At this point, you can sign in using the credentials you specified at the creation of the resource.
6. You’re now ready to start coding in R. You may browse the many sample notebooks that are available or you can create a new notebook by clicking on the R kernel button.
7. Remember to shut down your machine when you are not using it.
Note that if you are an educator and you want to use DSVMs for you R course, you have the chance to choose if providing all your students with a single DSVM, by sharing the credentials within the class or to provide every student with a single DSVM, finding the right trade-off for you among costs and flexibility.
Keep on learning
The example above covers only one of the possible functionalities enabled by DSVMs. You can also open a session in an interactive R console or coding within RStudio, which is pre-installed in the VM. In addition, you can leverage on other Azure services for data storage and modeling and you can share code with your team by using GitHub and the pre-installed Git clients: Git Bash and Git GUI. Find out more guidance on the DSVM documentation.
This article is contributed. See the original author and article here.
Link feature for Managed Instance is a new feature providing a hybrid connection between SQL Server 2016 (Enterprise, Developer and Standard editions) hosted anywhere and the fully managed PaaS service Azure SQL Managed Instance, providing unprecedented hybrid flexibility and database mobility. With an approach that uses near real-time data replication to Azure using Always On technology, you can offload workloads to read-only secondaries on Azure to take advantage of a fully managed database platform, performance, and scale. The link can be operated for as long as you need it – months and years at a time, empowering you to get all the modern benefits of Azure today without migrating to the cloud. On your modernization journey, when and if you are ready to migrate to the cloud, the link de-risks your migration experience allowing you to validate your workloads in Azure prior to migrating with a seamless and instant experience, and at your own pace. In this episode of Data Exposed with Dani Ljepava and Anna Hoffman, you’ll dive deeper into the insights of this new feature.
This article is contributed. See the original author and article here.
The Internet Systems Consortium (ISC) has released a security advisory that addresses a vulnerability affecting version 9.18.0 of ISC Berkeley Internet Name Domain (BIND). A remote attacker could exploit this vulnerability to cause a denial-of-service condition.
This article is contributed. See the original author and article here.
Manufacturing supply chains are experiencing a post-pandemic paradigm shift. As business models evolve to solve market disruptions, such as changing or adding direct-to-consumer (D2C) to a business-to-business (B2B) model, supply chains require agility and innovation to build resiliency and stay ahead of trends. United States D2C e-commerce sales have more than tripled in the past six years. 1 The market gained almost $100 billion in about half a decade from 2016 to 2021. It is expected to grow by nearly another $100 billion in the next three years, reaching $212.90 billion by 2024.1 With added supply chain complexities, including D2C, B2B, and advanced last-mile delivery bots, drones, and self-driving vehicles, manufacturers need strategies that go beyond cost reduction and can easily scale across the network. Supply chain networks able to scale their ecosystems will gain a competitive advantage, and the intelligent solutions to achieve this are composable and modular business applications.
Composable business applications are cloud-based technologies that provide unlimited scalability. They seamlessly integrate with other applications and are easy to use through a low-code or no-code interface. This composability allows organizations to combine different modular applications in an incremental and agile way through rapid sprints instead of embarking on time-consuming and costly rip-and-replace projects. Therefore, reach faster impact regardless of where manufacturers might be in their digital supply chain transformation journey.
The effectiveness of organizations’ digital supply chain in orchestrating supplier and fulfillment partnerships to meet anticipated customer demand determines the quality of the customer experience, as outlined in our on-demand webinar: A Smart Approach to Supply Chain Resilience Using Intelligent Order Management, featuring George Lawrie, Forrester Vice President and Principal Analyst.
To summarize, composability makes supply chain network innovation possible, as highlighted in the three advantages below.
1. Modern open platforms pave the way for innovation
Recent years show that siloed data has no place in today’s supply chain network. Traditional networks support predictable environments, which are no longer the case with current market dynamics. From raw material shortages to non-traditional emerging channels, supply chain networks require agility that extends to all stakeholders and foster fluid collaboration.
According to a Forrester report, platforms make it easier to assemble complex technology portfolios across a range of packaging alternatives, leveraging modular components while allowing for customization and custom development.2
Composable and modular business applications seamlessly integrate with existing enterprise systems with modern open platforms and low-code/no-code interfaces. In addition, they support innovative features such as predictive analytics or agile manufacturing for real-time and cross-channel inventory visibility. They also enable manufacturers to enhance network strategies and allow rapid deployment of digital supply chain control towers another advantage of composability.
2. End-to-end visibility to scale supply chain networks
Improving the customer experience is a top priority for many organizations. However, during the COVID-19 pandemic, we saw customer loyalty easily swayed. This trend gave new brand entrants a foothold into e-commerce and catalyzed many established brands to engage D2C to maintain market share. Similarly, the rise of the omnichannel’s ease of purchasing, delivery, and return options has set high customer expectations and introduced new buying behaviors. These dynamics present significant risks as ongoing supply chain disruptions continue. But extensible composable tools arm the supply chain network with another advantage: the digital control tower with end-to-end visibility to anticipate and mitigate order management turmoil.
According to a Forrester survey, the most common planned improvement (49 percent) retailers and CPG companies are making, is better visibility across the supply chain (from factories to raw materials suppliers).3 Consumer packaged goods (CPG) manufacturers with newly launched D2C business models recognize that complete visibility is essential for favorable customer experiences.
Digital supply chain control towers take real-time data from multiple supply chain network workloads and provide a holistic, multi-dimensional view. The partnership with FedEx and Microsoft Dynamics 365 for the cross-platform, logistics-as-a-service solution for brands exemplifies these capabilities, offering features like a seamless return experience and transportation optimization to proactively avoid delivery delays. Dynamics 365 Intelligent Order Management also allows organizations to set up the first step to incrementally build a composable digital supply chain platform with different module solutions for end-to-end visibility and deliver highly valued unified customer experiences.
3. Composability with AI drives actionable insights
Finally, composable business applications enable optimization at each node level letting manufacturers know where opportunities exist. Composable business applications give manufacturers advantages that are counterintuitive to previous assumptions. Advanced analytics powered by built-in AI and machine learning capabilities allow manufacturers to test hypotheses with predictive data-driven outcomes.
A Forrester survey shows that 56 percent of respondents report that one of the most important aspects of supply chain agility is increasing the use of machine learning and AI to drive process automation.3
An innovative supply chain network leverages composable digital tools with embedded AI and machine learning to improve decision-making, unify disparate data, foresee disruptions, and utilize deeper insights.
To start building composability in your supply chain network watch the video:
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Achieve more with agile and resilient supply chains
Dynamics 365 innovates supply chain networks, making them resilient and sustainable through composability. These intelligent solutions work seamlessly with enterprise resource planning (ERP) and customer relationship management (CRM) systems to respond to market dynamics quickly and integrate with many market-leading API-enabled applications. Pre-built connectors extend business capabilities through an ecosystem of specialized partners for order delivery, tax compliance, price calculations, transportation, and other logistics services. Plus, users can easily create rules and quickly configure order flows to adapt to changing market conditions or scale to support peak volume demands with an easy-to-use low-code/no-code interface.
By enabling these capabilities, manufacturers can accelerate the digital transformation of their order management process and turn order fulfillment into a competitive advantage. Users can also automate and optimize fulfillment using AI to create real-time inventory visibility and extend into a digital control tower for end-to-end network visibility.
At Microsoft, we are committed to empowering every person and organization on the planet to achieve more. With our next-generation digital supply chain applications, manufacturers can leverage composability to drive innovation across their supply chain networks. See how they can innovate yours in the on-demand webinar: A Smart Approach to Supply Chain Resilience Using Intelligent Order Management.
This article is contributed. See the original author and article here.
CISA has released an analysis and infographic detailing the findings from the 112 Risk and Vulnerability Assessments (RVAs) conducted across multiple sectors in Fiscal Year 2021 (FY21).
The analysis details a sample attack path comprising 11 successive tactics, or steps, a cyber threat actor could take to compromise an organization with weaknesses that are representative of those CISA observed in FY21 RVAs. The infographic highlights the three most successful techniques for each tactic that the RVAs documented. Both the analysis and the infographic map threat actor behavior to the MITRE ATT&CK® framework.
CISA encourages network defenders to review the analysis and infographic and apply the recommended mitigations to protect against the observed tactics and techniques. For information on CISA RVAs and additional services, visit the CISA Cyber Resource Hub.
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