ODSC - Open Data Science

JULY 19, 2023

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

For options 2, 3, and 4, the SageMaker Projects Portfolio provides project templates to run ML experiment pipelines, steps including data ingestion, model training, and registering the model in the model registry. You can choose which option to use depending on your setup.

ML 63

ML Data Scientist ML Engineer Data Science 63

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

ETL 59

ETL Categorization Automation Data Integration 59

AWS Machine Learning Blog

SEPTEMBER 11, 2024

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously.

ML 110

ML Deep Learning Algorithm Large Language Models 110

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment. Airflow for workflow orchestration Airflow schedules and manages complex workflows, defining tasks and dependencies in Python code. Saurabh Gupta is a Principal Engineer at Zeta Global.

Machine Learning 121

Machine Learning Data Scientist ML Data Ingestion 121

TransOrg Analytics

SEPTEMBER 24, 2024

AWS Data Exchange: Access third-party datasets directly within AWS. Data & ML/LLM Ops on AWS Amazon SageMaker: Comprehensive ML service to build, train, and deploy models at scale. Amazon EMR: Managed big data service to process large datasets quickly. Snowpark: Native support for data engineering and ML workflows.

ETL 52

ETL LLM Data Ingestion Automation 52

TransOrg Analytics

SEPTEMBER 24, 2024

AWS Data Exchange: Access third-party datasets directly within AWS. Data & ML/LLM Ops on AWS Amazon SageMaker: Comprehensive ML service to build, train, and deploy models at scale. Amazon EMR: Managed big data service to process large datasets quickly. Snowpark: Native support for data engineering and ML workflows.

ETL 52

ETL LLM Data Ingestion Automation 52

AWS Machine Learning Blog

NOVEMBER 7, 2024

We explored multiple big data processing solutions and decided to use an Amazon SageMaker Processing job for the following reasons: It’s highly configurable, with support of pre-built images, custom cluster requirements, and containers. When inference data is ingested on Amazon S3, EventBridge automatically runs the inference pipeline.

ML 113

ML Machine Learning Automation Data Scientist 113