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Visualization and other data that can help understand model behavior, applyįairness metrics, and make comparisons between algorithms to understand theīest variant to choose. Azure ML speeds up model creation with a convenient machine learning UI that allows you to build machine learning pipelines combining multiple algorithms, with steps like model training, testing, and evaluation.Īddition, Azure ML provides solutions for interpretable AI. It also provides an environment for consuming these algorithms and applying them to real-world tasks. This is a huge library of pre-packaged, pre-trained machine learning algorithms. You can also copy the data from Data Factory to Azure File Storage.
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Transform (ELT) strategies with no code or configuration using a visual editor.ĭata Factory provides built-in connectors with over 90 data sources including Amazon S3, Google BigQuery, and many on-premise data sources. Data Factory helps you build ETL and also Extract Load Structured database, cleans it, and converts the data into a format that is Old days of large-scale processing of structured data. Azure Data Factoryĭata Factory is an Extract Transform Load (ETL) service. Infrastructure in the form of monitoring, security, compliance, and highĪvailability via Azure redundancy options.
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It integrates with other Azure services likeĭata Factory and Data Lake Storage, allowing you to apply Hadoop analytics toĬomes with the full set of popular Hadoop tooling, including Apache Spark,Īpache Kafka, HBase, Hive, and Storm. HDInsight lets you quickly create big data clusters using Hadoop and scale them Perform complex, distributed analysis tasks on virtually any volume of data. Hadoop was a huge deal for big data in the previous decade, and while usage hasĭeclined, the Hadoop ecosystem is still incredibly powerful. Lets you set up managed Apache Spark clusters with auto-scaling andĪuto-termination, eliminating the complexity of setting up Spark in your local With Azure Machine Learning (see below), giving you access to a large number of Using any of these languages and frameworks. Libraries like TensorFlow and PyTorch, allowing you to work with Spark data Supports languages like Python, Scala, Java, SQL, and R, as well as AI/ML Used to process huge amounts of unstructured data at high speed. Is an analytics service based on Apache Spark. In addition, it provides the Azure Synapse Studio that offers a workspace for big data analysis and AI tasks and creates engaging visualizations of your data. It unifies all the data and lets you process and analyze it using the SQL language. It lets you load any number of data sources – both relational and non-relational databases, whether on-premise or in the Azure cloud. Azure Synapse AnalyticsĪzure Synapse Analytics is the next generation of Azure SQL Data Warehouse. If you’re using or considering Azure cloud services, this article can help you learn about eight popular big data analytics options on Microsoft Azure, what differentiates each service, and typical use cases for each option.