Because the ML model is deployed separately from the database and the application, each can scale up or scale out independently of the other. The integration between Aurora and each AWS ML service is optimized, delivering up to 100 times better throughput when compared to moving data between Aurora and SageMaker or Amazon Comprehend without this integration. When you run an ML query in Aurora using SQL, it can directly access a wide variety of ML models from Amazon SageMaker and Amazon Comprehend. Now, with a simple SQL query in Aurora, you can add ML to an enterprise application. Then, you called an ML service to run the model, reformat the output for your application, and finally load it into the application. Next, you had to prepare the data so it can be used by the ML model.
#Machine learning redshift code#
First, a data scientist had to build and train a model, then write the code to read data from the database. Previously, adding ML using data from Aurora to an application was a very complicated process. Machine Learning for database developersĪt re:Invent last year, we announced ML integrated inside Amazon Aurora for developers working with relational databases. Because all these use cases could benefit from ML, we’re adding ML capabilities to our purpose-built databases and analytics services so that database developers, data analysts, and business analysts can train models on their data or add inference results right from their database, without having to export and process their data or write large amounts of ETL code. You typically use several types of databases, data warehouses, and data lakes, to fit your use case. And you don’t just use one type of data store for all your needs. To meet the needs of this large and growing group of builders, we’re integrating ML into AWS databases, analytics, and business intelligence (BI) services.ĪWS customers generate, process, and collect more data than ever to better understand their business landscape, market, and customers.
The result is that ML isn’t being used as much as it can be. And even when you have the model in hand, there’s a long and involved process to prepare and move data to use the model. This group is typically proficient in SQL but not Python, and must rely on data scientists to build the models needed to add intelligence to applications or derive predictive insights from data. These users still find it too difficult and involved to extract meaningful insights from that data using ML. SageMaker has made ML model building and scaling more accessible to more people, but there’s a large group of database developers, data analysts, and business analysts who work with databases and data lakes where much of the data used for ML resides.
We launched Amazon SageMaker in 2017 to remove the challenges from each stage of the ML process, making it radically easier and faster for everyday developers and data scientists to build, train, and deploy ML models. For ML to have the broad impact that we think it can have, it has to get easier to do and easier to apply. Machine learning (ML) is becoming more mainstream, but even with the increasing adoption, it’s still in its infancy.
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