Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. Thus was born the data catalog. In our early days, “people” largely meant data analysts and business analysts. ML and DataOps teams). data pipelines) to support.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

Precisely

SEPTEMBER 18, 2023

Trusted data is crucial, and data observability makes it possible. Data observability is a key element of data operations (DataOps). Read Data Observability vs. Data Quality Given they share similar aims, it might be easy to conflate the idea of data observability with data quality.

Data Observability 52

Data Observability Data Quality Data Pipeline DataOps 52

Precisely

DECEMBER 12, 2023

Systems and data sources are more interconnected than ever before. A broken data pipeline might bring operational systems to a halt, or it could cause executive dashboards to fail, reporting inaccurate KPIs to top management. Data observability is a foundational element of data operations (DataOps).

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

BAIR

FEBRUARY 17, 2024

Optimization Often in ML, maximizing the quality of a compound system requires co-optimizing the components to work well together. Operation: LLMOps and DataOps. AI applications have always required careful monitoring of both model outputs and data pipelines to run reliably. for GPT-4 with 5-shot prompting or 83.7%

AI 145

AI AI DataOps Data Pipeline 145

BAIR

FEBRUARY 18, 2024

Optimization Often in ML, maximizing the quality of a compound system requires co-optimizing the components to work well together. Operation: LLMOps and DataOps. AI applications have always required careful monitoring of both model outputs and data pipelines to run reliably. for GPT-4 with 5-shot prompting or 83.7%

AI 40

AI AI DataOps Data Pipeline 40