AWS Machine Learning Blog

APRIL 17, 2023

You can integrate a Data Wrangler data preparation flow into your machine learning (ML) workflows to simplify data preprocessing and feature engineering, taking data preparation to production faster without the need to author PySpark code, install Apache Spark, or spin up clusters. Choose Python (Pandas). After notebook files (.ipynb)

AWS 100

AWS ML Python ML 100

BAIR

FEBRUARY 17, 2024

Python code that calls an LLM), or should it be driven by an AI model (e.g. DataOps: Because many AI systems involve data serving components like vector DBs, and their behavior depends on the quality of data served, any focus on operations for these systems should additionally span data pipelines. Operation: LLMOps and DataOps.

AI 145

AI AI DataOps Data Pipeline 145

Iguazio

JANUARY 16, 2024

Iguazio is an essential component in Sense’s MLOps and DataOps architecture, acting as the ML training and serving component of the pipeline. Flexibility : Open source MLRun (built and maintained by Iguazio) provides devs and scientists with a way to manage the full stack using Python. Gennaro Frazzingaro, Head of AI/ML at Sense.

ML 52

ML ML DataOps Data Scientist 52

Iguazio

JANUARY 16, 2024

Iguazio is an essential component in Sense’s MLOps and DataOps architecture, acting as the ML training and serving component of the pipeline. Flexibility : Open source MLRun (contributed and maintained by Iguazio) provides devs and scientists with a way to manage the full stack using Python. Gennaro Frazzingaro, Head of AI/ML at Sense.

ML 52

ML ML DataOps Data Scientist 52

BAIR

FEBRUARY 18, 2024

Python code that calls an LLM), or should it be driven by an AI model (e.g. DataOps: Because many AI systems involve data serving components like vector DBs, and their behavior depends on the quality of data served, any focus on operations for these systems should additionally span data pipelines. Operation: LLMOps and DataOps.

AI 40

AI AI DataOps Data Pipeline 40