AWS Machine Learning Blog

SEPTEMBER 18, 2023

Data scientists have to address challenges like data partitioning, load balancing, fault tolerance, and scalability. ML engineers must handle parallelization, scheduling, faults, and retries manually, requiring complex infrastructure code. Ingest the prepared data into the feature group by using the Boto3 SDK.

Machine Learning 118

Machine Learning ML Python Auto-complete 118

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

AWS Machine Learning Blog

SEPTEMBER 24, 2024

For production deployment, the no-code recipes enable easy assembly of the data ingestion pipeline to create a knowledge base and deployment of RAG or agentic chains. These solutions include two primary components: a data ingestion pipeline for building a knowledge base and a system for knowledge retrieval and summarization.

Data Ingestion 110

Data Ingestion Machine Learning Large Language Models Auto-complete 110