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Data Analytics Tutorial: Mastering Types of Statistical Sampling

Pickl AI

Simple Random Sampling Definition and Overview Simple random sampling is a technique in which each member of the population has an equal chance of being selected to form the sample. Analyze the obtained sample data. Analyze the obtained sample data. Select clusters randomly from the population.

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Apply fine-grained data access controls with AWS Lake Formation in Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction. compute.internal.

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Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning Blog

With Ray and AIR, the same Python code can scale seamlessly from a laptop to a large cluster. Amazon SageMaker Pipelines allows orchestrating the end-to-end ML lifecycle from data preparation and training to model deployment as automated workflows. Ray AI Runtime (AIR) reduces friction of going from development to production.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster. With this Spark connector, you can easily ingest data to the feature group’s online and offline store from a Spark DataFrame. To do so, open the notebook 4b-processing-rs-to-fs.ipynb in your SageMaker Studio environment.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

Connection definition JSON file When connecting to different data sources in AWS Glue, you must first create a JSON file that defines the connection properties—referred to as the connection definition file. The following is a sample connection definition JSON for Snowflake.

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Authoring custom transformations in Amazon SageMaker Data Wrangler using NLTK and SciPy

AWS Machine Learning Blog

In other words, companies need to move from a model-centric approach to a data-centric approach.” – Andrew Ng A data-centric AI approach involves building AI systems with quality data involving data preparation and feature engineering. Custom transforms can be written as separate steps within Data Wrangler.

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Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

AWS Machine Learning Blog

Amazon SageMaker distributed training jobs enable you with one click (or one API call) to set up a distributed compute cluster, train a model, save the result to Amazon Simple Storage Service (Amazon S3), and shut down the cluster when complete. In his spare time, he enjoys cycling, hiking, and complaining about data preparation.