AWS Machine Learning

NOVEMBER 16, 2023

With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production. Enable a data science team to manage a family of classic ML models for benchmarking statistics across multiple medical units.

Healthcare 87

Healthcare Government Engineering APIs 87

AWS Machine Learning

JUNE 24, 2022

The generated models are stored and benchmarked in the Amazon SageMaker model registry. The data is cataloged via the AWS Glue Data Catalog and shared with other users and accounts via AWS Lake Formation (the data governance layer). In the same account, Amazon SageMaker Feature Store can be hosted, but we don’t cover it this post.

Enterprise 88

Enterprise Engineering Accountability APIs 88

AWS Machine Learning

SEPTEMBER 1, 2023

The infrastructure code for all these accounts is versioned in a shared service account (advanced analytics governance account) that the platform team can abstract, templatize, maintain, and reuse for the onboarding to the MLOps platform of every new team. 15K available FM reference Step 1.

Engineering 101

Engineering Accountability Construction APIs 101

Pointillist

JULY 25, 2018

Pointillist can handle data in all forms, whether it is in tables, excel files, server logs, or 3rd party APIs. 3rd Party APIs: Pointillist has a large number of connectors using 3rd party APIs. The decision to conduct a paid pilot depends upon your company size, project scope, internal processes and governance.

Analytics 43

Analytics Metrics Government Journey mapping 43