KDnuggets

MARCH 17, 2020

As the role of the data engineer continues to grow in the field of data science, so are the many tools being developed to support wrangling all that data. Five of these tools are reviewed here (along with a few bonus tools) that you should pay attention to for your data pipeline work.

Data Engineering 395

Data Engineering Data Engineer Data Engineering Data Engineering 395

Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data. The Data Engineer Not everyone working on a data science project is a data scientist.

Data Science 130

Data Science Data Scientist ML ML 130

AWS Machine Learning Blog

APRIL 5, 2024

Phase 1: Data pipeline The Landsat 8 satellite captures detailed imagery of the area of interest every 15 days at 11:30 AM, providing a comprehensive view of the city’s landscape and environment. Data acquisition and preprocessing To implement the modules, Gramener used the SageMaker geospatial notebook within Amazon SageMaker Studio.

Clustering 134

Clustering ML ML AWS 134

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

AWS Machine Learning Blog

FEBRUARY 23, 2023

SageMaker has developed the distributed data parallel library , which splits data per node and optimizes the communication between the nodes. You can use the SageMaker Python SDK to trigger a job with data parallelism with minimal modifications to the training script. Each node has a copy of the DNN.

AWS 126

AWS ML ML Machine Learning 126

AWS Machine Learning Blog

DECEMBER 6, 2023

Second, the ability of these models to generate SQL queries from natural language has been proven for years, as seen in the 2020 release of Amazon QuickSight Q. We use the following Python script to recreate tables as pandas DataFrames. The details of implementing this approach in Python are described in Custom LLM Agent.

SQL 128

SQL AWS Analytics Analytics 128