2012, Data Preparation and ML - Data Science Current

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

Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. SageMaker Studio is the first fully integrated development environment (IDE) for ML. You can use query_string to filter your dataset by SQL and unload it to Amazon S3.

ML

ML ML AWS Data Warehouse

Govern generative AI in the enterprise with Amazon SageMaker Canvas

AWS Machine Learning Blog

SEPTEMBER 23, 2024

Launched in 2021, Amazon SageMaker Canvas is a visual point-and-click service that allows business analysts and citizen data scientists to use ready-to-use machine learning (ML) models and build custom ML models to generate accurate predictions without writing any code. This way, users can only invoke the allowed models.

AI

AI AI ML ML

Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning Blog

SEPTEMBER 3, 2024

With the introduction of EMR Serverless support for Apache Livy endpoints , SageMaker Studio users can now seamlessly integrate their Jupyter notebooks running sparkmagic kernels with the powerful data processing capabilities of EMR Serverless. This same interface is also used for provisioning EMR clusters. elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*"

AWS

AWS Clustering Big Data Big Data

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Apache Airflow®: The Ultimate Guide to DAG Writing

The 2nd Generation of Innovation Management: A Survival Guide

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Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML

AWS Machine Learning Blog

AUGUST 4, 2023

Train a recommendation model in SageMaker Studio using training data that was prepared using SageMaker Data Wrangler. The real-time inference call data is first passed to the SageMaker Data Wrangler container in the inference pipeline, where it is preprocessed and passed to the trained model for product recommendation.

ML

ML ML AWS AI

Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning Blog

AUGUST 22, 2023

Machine learning (ML) is revolutionizing solutions across industries and driving new forms of insights and intelligence from data. Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed.

Machine Learning

Machine Learning Machine Learning AWS ML

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Flipboard

MARCH 7, 2023

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity.

Python

Python AWS ML ML

Why is Git Not the Best for ML Model Version Control

The MLOps Blog

NOVEMBER 30, 2022

Data science teams currently struggle with managing multiple experiments and models and need an efficient way to store, retrieve, and utilize details like model versions, hyperparameters, and performance metrics. ML model versioning: where are we at? The short answer is we are in the middle of a data revolution.

ML

ML ML Machine Learning Machine Learning

Fine-tune multimodal models for vision and text use cases on Amazon SageMaker JumpStart

AWS Machine Learning Blog

NOVEMBER 15, 2024

jpg", "prompt": "Which part of Virginia is this letter sent from", "completion": "Richmond"} SageMaker JumpStart SageMaker JumpStart is a powerful feature within the SageMaker machine learning (ML) environment that provides ML practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs).

ML

ML ML Python AWS

A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

AWS

AWS ML ML Clustering

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

AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them.

SQL

SQL AWS Database Data Scientist

A Guide to Convolutional Neural Networks

Heartbeat

AUGUST 21, 2023

AlexNet is a more profound and complex CNN architecture developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. The data should be split into training, validation, and testing sets. It has eight layers, five of which are convolutional and three fully linked. We pay our contributors, and we don’t sell ads.

Natural Language Processing

Natural Language Processing Deep Learning Deep Learning ML

Reflecting on a decade of data science and the future of visualization tools

Tableau

FEBRUARY 24, 2021

As data science work grew in complexity, data scientists became less generalized and more specialized, often engaged in specific aspects of data science work. as early as 2012 already identified this trend, which has only accelerated over time. Interviews conducted by Harris et al.

Data Science

Data Science Data Scientist Data Visualization Computer Science

Reflecting on a decade of data science and the future of visualization tools

Tableau

FEBRUARY 24, 2021

As data science work grew in complexity, data scientists became less generalized and more specialized, often engaged in specific aspects of data science work. as early as 2012 already identified this trend, which has only accelerated over time. Interviews conducted by Harris et al.

Data Science

Data Science Data Scientist Data Visualization Computer Science

Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas

AWS Machine Learning Blog

OCTOBER 28, 2024

In the modern, cloud-centric business landscape, data is often scattered across numerous clouds and on-site systems. This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives.

Machine Learning

Machine Learning Machine Learning ML ML

Introducing the DataRobot AI Cloud: A Closer Look

DataRobot

SEPTEMBER 14, 2021

Since DataRobot was founded in 2012, we’ve been committed to democratizing access to the power of AI. We’re building a platform for all users: data scientists, analytics experts, business users, and IT. Let’s dive into each of these areas and talk about how we’re delivering the DataRobot AI Cloud Platform with our 7.2

AI

AI AI Data Pipeline Data Preparation

Data Science Current

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

Govern generative AI in the enterprise with Amazon SageMaker Canvas

Webinars

Trending Sources

Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

Webinars

Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML

Machine learning with decentralized training data using federated learning on Amazon SageMaker

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Why is Git Not the Best for ML Model Version Control

Fine-tune multimodal models for vision and text use cases on Amazon SageMaker JumpStart

A review of purpose-built accelerators for financial services

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

A Guide to Convolutional Neural Networks

Reflecting on a decade of data science and the future of visualization tools

Reflecting on a decade of data science and the future of visualization tools

Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas

Introducing the DataRobot AI Cloud: A Closer Look

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