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description: Guidance on a set of objectives that are typically used to implement comprehensive data science solutions with Azure technologies using the Team Data Science Process and Azure Machine Learning.
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| | [HDInsight](../../hdinsight/hdinsight-hadoop-introduction.md) | HDInsight is the Hadoop open-source infrastructure, available as a service in Microsoft Azure. Your Machine Learning algorithms may involve large sets of data, and HDInsight has the ability to store, transfer and process data at large scale. This article covers working with HDInsight. | [Create a small HDInsight cluster](../../hdinsight/hdinsight-hadoop-create-linux-clusters-portal.md). Use HiveQL statements [to project columns onto an /example/data/sample.log file](../../hdinsight/hdinsight-use-hive.md). Alternatively, [you can complete this knowledge check on your local system](../../hdinsight/hdinsight-hadoop-emulator-get-started.md). |
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| Create a Data Processing Flow from Business Requirements | [Determining the Question, following the TDSP](https://buckwoody.wordpress.com/2017/08/31/the-keys-to-effective-data-science-projects-the-question/) | With the development environment installed and configured, and the understanding of the technologies and processes in place, it’s time to put everything together using the TDSP to perform an analysis. We need to start by defining the question, selecting the data sources, and the rest of the steps in the Team Data Science Process. Keep in mind the DevOps process as we work through this process. In this article, you learn how to take the requirements from your organization and create a data flow map through your application to define your solution using the Team Data Science Process.
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| Locate a resource on “[The 5 data science questions](../studio/data-science-for-beginners-the-5-questions-data-science-answers.md)” and describe one question your organization might have in these areas. Which algorithms should you focus on for that question? |
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| Use Azure Machine Learning to create a predictive solution |[Azure Machine Learning](../overview-what-is-azure-ml.md)| Microsoft Azure Machine Learning includes working with data sources, using AI for data wrangling and feature engineering, creating experiments, and tracking model runs. All of this works in a single environment and most functions can run locally or in Azure. You can use the PyTorch, TensorFlow, and other frameworks to create your experiments. In this article, we focus on a complete example of this process, using everything you’ve learned so far. ||
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| Use Power BI to visualize results | [Power BI](https://powerbi.microsoft.com/guided-learning/) | Power BI is Microsoft’s data visualization tool. It is available on multiple platforms from Web to mobile devices and desktop computers. In this article you learn how to work with the output of the solution you’ve created by accessing the results from Azure storage and creating visualizations using Power BI. | [Complete this tutorial on Power BI.](https://powerbi.microsoft.com/documentation/powerbi-service-get-started/) Then connect Power BI to the Blob CSV created in an experiment run. |
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| Use Azure Machine Learning to create a predictive solution |[Azure Machine Learning](../overview-what-is-azure-ml.md)| Microsoft Azure Machine Learning uses AI for data wrangling and feature engineering, manages experiments, and tracks model runs. All of this works in a single environment and most functions can run locally or in Azure. You can use the PyTorch, TensorFlow, and other frameworks to create your experiments. In this article, we focus on a complete example of this process, using everything you’ve learned so far. ||
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| Use Power BI to visualize results | [Power BI](https://powerbi.microsoft.com/guided-learning/) | Power BI is Microsoft’s data visualization tool. It is available on multiple platforms from Web to mobile devices and desktop computers. In this article, you learn how to work with the output of the solution you’ve created by accessing the results from Azure storage and creating visualizations using Power BI. | [Complete this tutorial on Power BI.](https://powerbi.microsoft.com/documentation/powerbi-service-get-started/) Then connect Power BI to the Blob CSV created in an experiment run. |
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| Monitor your Solution | [Application Insights](../../azure-monitor/app/app-insights-overview.md) | There are multiple tools you can use to monitor your end solution. Azure Application Insights makes it easy to integrate built-in monitoring into your solution. | [Set up Application Insights to monitor an Application](https://cmatskas.com/visual-studio-code-integration-with-azure-application-insights/). |
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| | [Azure Monitor logs](../../log-analytics/log-analytics-overview.md) | Another method to monitor your application is to integrate it into your DevOps process. The Azure Monitor logs system provides a rich set of features to help you watch your analytic solutions after you deploy them. | [Complete this tutorial](https://docs.loganalytics.io/docs/Learn/Getting-Started/Getting-started-with-the-Analytics-portal) on using Azure Monitor logs. |
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| Complete this Learning Path || Congratulations! You’ve completed this learning path. There is a lot more to learn.|
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| Complete this Learning Path || Congratulations! You’ve completed this learning path. |
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## Next steps
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[Team Data Science Process for Developer Operations](team-data-science-process-for-devops.md) This article explores the Developer Operations (DevOps) functions that are specific to an Advanced Analytics and Cognitive Services solution implementation.
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