We are really excited to introduce the preview of new machine learning experiences in Azure Synapse Analytics, to make it easier for data professionals to enrich data and build predictive analytics solutions.
AI and machine learning is an important aspect of any analytics solution. By integrating Azure Synapse Analytics with Azure Machine Learning and Azure Cognitive Services, we are bringing together the best of two worlds, to empower data professionals with the power of predictive analytics and AI. Data engineers working in Azure Synapse can access models in Azure Machine Learning’s central model registry, created by data scientists. Data engineers can also build models with ease in Azure Synapse, using the code-free automated ML powered by Azure Machine Learning and use these models to enrich data.
Linked services can be created to enable seamless collaboration across an Azure Synapse and an Azure Machine Learning workspace. Linked workspaces allow data professionals in Synapse to leverage new machine learning experiences aiming to make it easier to collaborate across Synapse and Azure ML.
Create the Azure Machine Linked service in your Synapse workspace
Data professionals working in Azure Synapse can collaborate seamlessly with ML professionals who create models in Azure Machine Learning. These models can be shared and deployed directly in Azure Synapse for enrichment of data.
In the model scoring wizard, enrich with an existing model
By supporting the portable ONNX model format, users can bring a variety of models to Synapse for performant batch scoring, right where the data lives. This removes the need for data movement and ensures that the data remains within the security boundaries defined by Azure Synapse. Columns containing predicted values can easily be appended to the original views and tables that are used to populate your Power BI reports.
Fully integrated data enrichment capabilities powered by Azure Cognitive Services allow Synapse users to enrich data and gain insights by leveraging state of the art pre-trained AI models. The first two models available through the Synapse workspace are Text Analytics (Sentiment Analysis) and Anomaly detector. In the future you’ll see more pre-trained models available for use.
Leverage Azure Cognitive Services in Azure Synapse for sentiment analysis
Leverage Azure Cognitive Services in Azure Synapse for Anomaly detection
Data professionals can also build models with ease in Azure Synapse, using code-free automated ML powered by Azure Machine Learning. These Automated ML runs will be executed on Synapse serverless Apache Spark pools and tracked in the Azure Machine Learning service.
Select the task type for your Automated ML run in Azure Synapse
All the machine learning experiences in Azure Synapse produce code artifacts such as PySpark Notebooks or SQL scripts, that allow users of all skill levels to easily operationalize their work in data integration pipelines, to support end-to-end analytics flows from a single unified Synapse experience.
The integration between Azure Synapse Analytics and Azure AI promotes seamless collaboration between data and ML teams to develop predictive analytics solutions. With these new experiences in Synapse Studio, teams of all skill levels can leverage machine learning to analyze and enrich data and deliver greater analytics insights. Join customers already taking advantage of predictive analytics solutions and get started on your journey by using the links provided below.
AzureSynapse ML Docs overview: https://aka.ms/synapseMLDocs
Azure Synapse Automated ML tutorial: https://aka.ms/SynapseMLDocs_AutoML_tutorial
Azure Synapse model scoring tutorial: https://aka.ms/SynapseMLDocs_Scoring_Tutorial
Azure Synapse Cognitive Services tutorial: https://aka.ms/SynapseML_Docs_Cognitive_Services
Link Azure Synapse workspace to Azure ML workspace:https://aka.ms/SynapseMLDocs_link_AML
Azure Synapse TechCommunity: Check this blog daily to see a roundup of all the new tutorial blogs that will be posted for the next two weeks.
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