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Databricks MLOps - Deploy Machine Learning Model On Azure

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Databricks MLOps - Deploy Machine Learning Model On Azure In this little video series I'll get to the bottom of how you can control the Azure Databricks platform with your DevOps toolbox. In this part we're deploying the Machine Learning Model, which we created in part 2, first as Azure Container Instance and then to the Azure Kubernetes Service. For this purpose, we use the Azure Machine Learning Service to create containerised Web API. Everything will be - of course - completely automated by using our Azure DevOps pipeline. If you haven't yet seen the first video in this series, I strongly recommend that you do so: https://www.youtube.com/watch?v NLXis... Subscribe for more free data analytics videos: https://www.youtube.com/saschadittman...



A Decade of Microsoft Azure: From its Creation to its Many Successful Transformations

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Windows Azure--no, that was not a typo... "Windows" and not "Microsoft" just yet--had its debut in 2008 at The Professional Developers Conference (PDC). Ray Ozzie, Microsoft's former chief software architect, revealed Windows Azure as "a service, hosted and maintained by Microsoft on an array of distributed data centers. Then two years later--on February 1, 2010-- it was generally available. The operating system designed to become Microsoft's cloud launched as a PaaS offering (Platform as a Service). A community of developers was the first to build a precise class of web applications.


At Ignite, Microsoft convincingly addresses the multicloud imperative - SiliconANGLE

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Microsoft Corp. has fresh momentum in the cloud wars. For starters, Microsoft was recently awarded the U.S. Department of Defense's Joint Enterprise Defense Infrastructure or JEDI contract. For another, the company has just announced a broad range of enhancements to its Azure cloud portfolio that will almost certainly help to win it new business in the tooth-and-nail battle with Amazon Web Services, Google Cloud Platform and others. This week at its Ignite 2019 conference, Microsoft launched both new and enhanced data-centric solutions for its cloud solution portfolio. For information technology professionals managing their firms' investments in cloud technologies, these were the chief announcements of interest at Ignite 2019: For developers and users, the chief announcements addressed multicloud-spanning data warehousing (the new Azure Synapse Analytics, the evolution of Azure SQL Data Warehouse), automated data-science DevOps workflows (a.k.a.


Quick Guide to Cloud for Start Ups – Founders Time – Medium

@machinelearnbot

This article will focus on side by side comparison of cloud computing services offered by three major providers: Amazon Web Services (AWS), Google Cloud Computing (GCP) and Microsoft Azure (Azure). In addition to these there, there are also a number of alternative providers: IBM Cloud, Oracle, Alibaba Cloud, Heroku and others. Rather than reciting the general information on cloud services, this article will primarily focus on pointing the reader to the right place on the internet for information. Before we start, it is important to understand the difference between IaaS, PaaS and SaaS. This AWS article explains it very well.