AWS Machine Learning Blog

FEBRUARY 27, 2024

The SageMaker project template includes seed code corresponding to each step of the build and deploy pipelines (we discuss these steps in more detail later in this post) as well as the pipeline definition—the recipe for how the steps should be run. Workflow B corresponds to model quality drift checks.

Machine Learning 129

Machine Learning DevOps Data Scientist Data Quality 129

The MLOps Blog

MARCH 15, 2023

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. But there is definitely room for improvement in our deployment as well.

ETL 52

ETL Data Drift Machine Learning ML 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

The MLOps Blog

AUGUST 3, 2023

And then, we’re trying to boot out features of the platform and the open-source to be able to take Hamilton data flow definitions and help you auto-generate the Airflow tasks. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter. I term it as a feature definition store.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

Snorkel AI

MAY 31, 2023

Those pillars are 1) benchmarks—ways of measuring everything from speed to accuracy, to data quality, to efficiency, 2) best practices—standard processes and means of inter-operating various tools, and most importantly to this discussion, 3) data. In order to do this, we need to get better at measuring data quality.

Data Drift 52

Data Drift Machine Learning ML Data Quality 52