AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

Data Warehouse 113

Data Warehouse Machine Learning Machine Learning Cloud Data 113

Flipboard

AUGUST 17, 2023

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster.

ML 123

ML ML AWS Data Warehouse 123

AWS Machine Learning Blog

SEPTEMBER 18, 2023

With Ray and AIR, the same Python code can scale seamlessly from a laptop to a large cluster. Amazon SageMaker Pipelines allows orchestrating the end-to-end ML lifecycle from data preparation and training to model deployment as automated workflows. You can specify resource requirements in actors too.

Machine Learning 130

Machine Learning Machine Learning ML ML 130

phData

DECEMBER 4, 2023

Thirty seconds is a good default for human users; if you find that queries are regularly queueing, consider making your warehouse a multi-cluster that scales on-demand. Cluster Count If your warehouse has to serve many concurrent requests, you may need to increase the cluster count to meet demand.

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Clustering Database SQL Data Pipeline 98

Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

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. Flyte Flyte is a platform for orchestrating ML pipelines at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

phData

FEBRUARY 7, 2024

Server Side Execution Plan When you trigger a Snowpark operation, the optimized SQL code and instructions are sent to the Snowflake servers where your data resides. This eliminates unnecessary data movement, ensuring optimal performance. Snowflake spins up a virtual warehouse, which is a cluster of compute nodes, to execute the code.

Python 52

Python ML ML SQL 52