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Azure Flash News Episode #85

  • 02/04/2020

Azure Flash News: Watch Episode

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Azure Cost Management updates – January 2020

https://azure.microsoft.com/en-us/blog/azure-cost-management-updates-january-2020/

Whether you're a new student, thriving startup, or the largest enterprise, you have financial constraints and you need to know what you're spending, where, and how to plan for the future. Nobody wants a surprise when it comes to the bill, and this is where Azure Cost Management comes in.

MLOps—the path to building a competitive edge

https://azure.microsoft.com/en-us/blog/mlops-the-path-to-building-a-competitive-edge/

Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models.

Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. What is MLOps ? It is the practice of collaboration between data science and IT teams designed to accelerate the entire machine lifecycle across model development, deployment, monitoring, and more. Microsoft Azure Machine Learning enables companies to fully embrace MLOps practices will and truly be able to realize the potential of AI in their business.

10 recommendations for cloud privacy and security with Ponemon research

https://azure.microsoft.com/en-us/blog/10-recommendations-for-cloud-privacy-and-security-with-ponemon-research/

Today we’re pleased to publish Data Protection and Privacy Compliance in the Cloud: Privacy Concerns Are Not Slowing the Adoption of Cloud Services, but Challenges Remain, original research sponsored by Microsoft and independently conducted by the Ponemon Institute. The report concludes with a list of 10 recommended steps that organizations can take to address cloud privacy and security concerns, and in this blog, we have provided information about Azure services such as Azure Active Directory and Azure Key Vault that help address all 10 recommendations.

Azure Blueprint for FedRAMP High now available in new regions

https://azure.microsoft.com/en-us/updates/azure-blueprint-for-fedramp-high-is-now-available-for-azure-government-and-azure-public/

The Azure Blueprint for FedRAMP High is now available in both Azure Government and Azure Public regions. This is in addition to the Azure Blueprint for FedRAMP Moderate released in November, 2019.

Azure Blueprints is a free service used by cloud architects and central information technology groups to define a set of Azure resources that implements and adheres to an organization's standards, patterns, and requirements. Azure Blueprints makes it possible for development teams to rapidly build and stand up new trusted environments within organizational compliance requirements. The new FedRAMP High blueprint can be applied to new subscriptions as well as existing environments.

Data Factory Adds Managed Identity Support to Data Flows

https://azure.microsoft.com/en-us/updates/data-factory-adds-managed-identity-support-to-data-flows/

Azure Data Factory users can now build Mapping Data Flows utilizing Managed Identity (formerly MSI) for Azure Data Lake Store Gen 2, Azure SQL Database, and Azure Synapse Analytics (formerly SQL DW). These added security features, combined with ADF's existing support for Azure Trusted Services, will allow you to now build ETL pipelines using ADLS Gen 2 storage accounts as sources and sinks without requiring public endpoints.

Here are documentation links to configure your data factory to use MSI with ADLS Gen 2, SQL DB, and Synapse Analytics (SQL DW).

Azure Stream Analytics—Machine learning–based anomaly detection functions

https://azure.microsoft.com/en-us/updates/machine-learningbased-anomaly-detection-functions-in-azure-stream-analytics-general-availability/

Easily add anomaly detection capabilities to your Stream Analytics jobs without the requirement to develop and train your own machine learning models. Ready-to-use unsupervised learning ML models are provided within the SQL language. This reduces the cost and complexity associated with building and training ML models to a simple single function call.

Thanks

Produced by Emily Mackmiller

MTC Facility

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