AWS Machine Learning Blog

APRIL 16, 2024

In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.

SQL 116

SQL AWS Database Data Scientist 116

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. You can use query_string to filter your dataset by SQL and unload it to Amazon S3.

ML 123

ML ML AWS Data Warehouse 123

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 94

ML ML AWS AI 94

Flipboard

MARCH 21, 2025

The retail team has created a project retailsales-sql-project and the data analysts team has created a project dataanalyst-sql-project within SageMaker Unified Studio. Create a SageMaker Unified Studio domain and three projects using the SQL analytics project profile. See Create a new project to create a project.

SQL 136

SQL Data Analyst Data Warehouse AWS 136

Dataconomy

MARCH 5, 2025

Job title history of data scientist The title “data scientist” gained prominence in 2008 when companies like Facebook and LinkedIn utilized it in corporate job descriptions. Citizen Data Scientist: Uses existing analytics tools but may lack formal training and earn a salary more aligned with general activities.

Data Scientist 91

Data Scientist Citizen Data Scientist Exploratory Data Analysis Machine Learning 91

AWS Machine Learning Blog

OCTOBER 28, 2024

This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects. You can use SageMaker Canvas to build the initial data preparation routine and generate accurate predictions without writing code.

Machine Learning 104

Machine Learning Machine Learning ML ML 104