Microsoft Machine Learning Library for Apache Spark Available Now

Spark connector for Azure Cosmos DB is now truly multi-model, Video setup guides for Windows Analytics Upgrade Readiness published.

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Microsoft Machine Learning library, designed to help data scientists be more productive, is available on Wednesday for cluster computing system Apache Spark, allowing users to be more productive and focus on the data science.

The library aims to increase the rate of experimentation and leverage cutting-edge machine learning techniques, including deep learning, on large datasets. It provides simplified, consistent application programming interfaces (APIs) for handling different types of data.

Microsoft Machine Learning for Apache Spark (MMLSpark) simplifies many common tasks for building models in PySpark.

Additionally, Spark Connector is now also made available for Azure Cosmos DB, that provides seamless interaction with globally distributed, multimodel data

The Spark connector for Azure Cosmos Database (DB), a truly multimodel , while Azure Cosmos DB is industry's "first globally distributed, multimodel database service," Denny Lee says.

Lee providing details on how Spark Connector for Azure Cosmos DB can seamlessly interact with multiple data models supported by Azure Cosmos DB. "Apache Spark with Azure Cosmos DB enables both ad hoc, interactive queries on big data, as well as advanced analytics, data science, machine learning and artificial intelligence," Lee writes.

Here're some use-cases of Azure Cosmos DB + Spark:

  • Streaming Extract, Transformation, and Loading of data (ETL)
  • Data enrichment
  • Trigger event detection
  • Complex session analysis and personalization
  • Visual data exploration and interactive analysis
  • Notebook experience for data exploration, information sharing, and collaboration

The Spark Connector for Azure Cosmos DB uses Azure DocumentDB Java SDK. To get started download the Spark connector from GitHub.

With new Power BI sample model, you can try Azure Analysis Services without needing to build anything

Self-service business intelligence (BI) tools have made big strides in making data accessible to business users, starting today, business users can use Azure Analysis Services without the need to build anything, using a new sample model based on the Adventure Works Internet Sales database, Josh Caplan says.

"With Azure Analysis Services, a BI professional can create a semantic model over the raw data and share it with business users so that all they need to do is connect to the model from any BI tool and immediately explore the data and gain insights," he writes.

Self-service BI model for Azure Analysis Services
Self-service BI model for Azure Analysis Services

Setup guides for Windows Analytics upgrade readiness published on Wednesday, walks through how to use, setup and confifure this new service.

For those new, "Upgrade Readiness helps to collect system, application, and driver data—and identify compatibility issues that can block an upgrade. The service walks you through the discovery and rationalization process, and produces a detailed computer and application inventory, offers application usage information, suggests fixes for application and driver compatibility issues (when they are known to Microsoft), and produces a list of devices that are ready to be upgraded."

Watch the videos below to get started with the new Windows Analytics Upgrade Readiness service:

In this video below, find out how the new service can help you gain insight into, and recommendations about, organization's readiness to upgrade devices to the latest version of Windows, and provide a guided workflow that will help you efficiently manage your upgrade process from end to end.

In this second video, learn how to set up Upgrade Readiness in your organization:

This final video provide step-by-step instructions on how to prioritize application and driver issues, assign and track issue resolution, and easily identify which computers in your organization are ready for upgrade: