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      Machine Learning

      Azure Machine Learning was a separate service, but it was possible to integrate it with Azure Synapse Analytics to enable machine learning capabilities within Synapse Analytics workflows. Since technology and services evolve rapidly, please verify the current state of integration and features.

      Here’s an overview of how Azure Machine Learning can be used within Azure Synapse Analytics:

      – Integration: Azure Machine Learning can be integrated into Azure Synapse Analytics to leverage the power of machine learning models in your analytics and data processing workflows. This integration allows you to access machine learning capabilities directly within Synapse Studio, the unified workspace for Synapse Analytics.

      – Data Preparation: Within Synapse Studio, you can prepare your data by using data wrangling, transformation, and feature engineering tools. This is crucial as high-quality data is essential for training and deploying machine learning models.

      – Model Training: Azure Machine Learning within Synapse Analytics lets you create and train machine learning models using a variety of algorithms and frameworks. You can select and configure the machine learning model that best suits your use case and data. Training can be done on a variety of data sources, including data stored in data lakes, data warehouses, and streaming data.

      – Model Deployment: Once you’ve trained your machine learning models, you can deploy them within Synapse Analytics. These models can be used to make predictions on new data, allowing you to operationalize your machine learning solutions.

      – Automated Machine Learning (AutoML): Azure Machine Learning offers AutoML capabilities, which can be used to automate the process of selecting the best machine learning model and hyperparameters. You can use AutoML to streamline the model-building process and find the best-performing model for your data.