Amazon SageMaker Studio is the premier integrated development environment (IDE) for machine learning (ML). With a simple click, data scientists and developers can swiftly launch Studio notebooks to delve into datasets and create models. Now, you can leverage Studio notebooks to securely connect with Amazon EMR clusters, facilitating the preparation of extensive data for analysis.
Data Processing Solutions for AI/ML
This blog entry was updated in June 2022 to incorporate new features related to data processing, such as the integration of Amazon SageMaker Studio with EMR. Training a precise machine learning model involves numerous steps, but none are as crucial as data processing. Processing stages may include converting data formats, cleaning data, and more.
Accessing Data Sources from Amazon SageMaker R Kernels
Amazon SageMaker notebooks now natively support R without requiring manual installation of R kernels. The notebooks also come pre-loaded with the reticulate library, providing an R interface for the Amazon SageMaker Python SDK, enabling you to call Python modules from an R script. This makes it easy to run machine learning tasks seamlessly.
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Exploring Data Warehouse Tables with Machine Learning and Amazon SageMaker Notebooks
Are you a data scientist eager to explore data warehouse tables within your ML environment? If so, this guide illustrates how to conduct exploratory analysis on large datasets stored in your data warehouse and cataloged in the AWS Glue Data Catalog through your Amazon SageMaker notebooks.
Building Amazon SageMaker Notebooks Supported by Spark in Amazon EMR
This blog post was last reviewed in August 2022. Introduced at AWS re:Invent in 2017, Amazon SageMaker provides a fully managed service for data science and machine learning workflows. One pivotal feature of Amazon SageMaker is its robust Jupyter notebook interface, which can be utilized to construct models effectively. You can enhance the capabilities of Amazon SageMaker by integrating it with Spark.
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Distributed Inference Using Apache MXNet and Apache Spark on Amazon EMR
This blog entry demonstrates how to perform distributed offline inference on large datasets using Apache MXNet and Apache Spark on Amazon EMR. We discuss the utility of offline inference, the challenges it presents, and how leveraging MXNet and Spark on Amazon EMR can help you navigate these challenges.
Running Deep Learning Frameworks with GPU Instance Types on Amazon EMR
AWS has announced support for Apache MXNet and new GPU instance types on Amazon EMR, allowing you to run distributed deep neural networks alongside your machine learning workflows and big data processing tasks. Moreover, you can install and utilize custom deep learning libraries on your EMR clusters equipped with GPU hardware.
Building PMML-Based Applications and Generating Predictions in AWS
If you create machine learning models, you’re familiar with the challenge of exporting and importing them into different frameworks to separate model generation from prediction. Many applications utilize PMML (Predictive Model Markup Language) to transfer ML models across frameworks. PMML serves as an XML representation of a data mining model.
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