ODSC - Open Data Science

FEBRUARY 22, 2024

By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows. Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting Feature engineering from raw entity-level data is complex, but there are ways to reduce that complexity.

Machine Learning 52

Machine Learning Data Science Python Data Ingestion 52

The MLOps Blog

AUGUST 11, 2023

Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. 1 Data Ingestion (e.g.,

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Metadata 134

Metadata Machine Learning Data Scientist Data Quality 134