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Tag: Apache Spark

Connect Amazon EMR and RStudio on Amazon SageMaker

RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the...

Run secure processing jobs using PySpark in Amazon SageMaker Pipelines

Amazon SageMaker Studio can help you build, train, debug, deploy, and monitor your models and manage your machine learning (ML) workflows. Amazon SageMaker Pipelines...

Investing in Coactive

Understanding what’s in an image — one of the simplest cognitive tasks for most humans — is a stubbornly difficult problem for artificial intelligence...

Accelerate time to insight with Amazon SageMaker Data Wrangler and the power of Apache Hive

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon...

Prepare data from Amazon EMR for machine learning using Amazon SageMaker Data Wrangler

Data preparation is a principal component of machine learning (ML) pipelines. In fact, it is estimated that data professionals spend about 80 percent of...

Apply fine-grained data access controls with AWS Lake Formation and Amazon EMR from Amazon SageMaker Studio

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables data scientists and developers to perform every step...

Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

Organizations are using machine learning (ML) and AI services to enhance customer experience, reduce operational cost, and unlock new possibilities to improve business outcomes....

Load and transform data from Delta Lake using Amazon SageMaker Studio and Apache Spark

Data lakes have become the norm in the industry for storing critical business data. The primary rationale for a data lake is to land all types of data, from raw data to preprocessed and postprocessed data, and may include both structured and unstructured data formats. Having a centralized data store for all types of data […]

3 Strategies for Creating a Successful MLOps Environment

Disconnects between development, operations, data engineers, and data science teams might be holding your organization back from extracting value from its artificial intelligence (AI) and machine learning (ML) processes. In short, you may be missing the most essential ingredient of a successful MLOps environment: collaboration. For instance, your data scientists might be using tools like JupyterHub or […]

The post 3 Strategies for Creating a Successful MLOps Environment appeared first on DATAVERSITY.

Data Science & Analytics Industry Main Developments in 2021 and Key Trends for 2022

We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.

Build accurate ML training datasets using point-in-time queries with Amazon SageMaker Feature Store and Apache Spark

This post is co-written with Raphey Holmes, Software Engineering Manager, and Jason Mackay, Principal Software Development Engineer, at GoDaddy. GoDaddy is the world’s...

Graph database platform Neo4j raises $325M to inform decision-making

Transform 2021 Elevate your enterprise data technology and strategy. July 12-16 Register Today Elevate your enterprise data...

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