phData

DECEMBER 4, 2023

More on this topic later; but for now, keep in mind that the simplest method is to create a naming convention for database objects that allows you to identify the owner and associated budget. The extended period will allow you to perform Time Travel activities, such as undropping tables or comparing new data against historical values.

Clustering 98

Clustering Database SQL Data Pipeline 98

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. Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster.

ML 93

ML ML AWS Data Warehouse 93

The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Dolt Dolt is an open-source relational database system built on Git.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

phData

FEBRUARY 7, 2024

The following example uses a dict containing connection parameters to create a new session: connection_parameters = { "account": " ", "user": " ", "password": " ", "role": " ", # optional "warehouse": " ", # optional "database": " ", # optional "schema": " ", # optional } new_session = Session.builder.configs(connection_parameters).create()

Python 52

Python ML ML SQL 52

DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. And these are not really compute-intensive for most structured ML problems.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. And these are not really compute-intensive for most structured ML problems.

SQL 52

SQL ML ML Python 52