Iguazio

JANUARY 16, 2024

This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers. The Challenge Like many organizations, the AI/ML team at Sense was finding it challenging to scale its ML operations.

ML 52

ML ML DataOps Data Scientist 52

Iguazio

JANUARY 16, 2024

This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers. The Challenge: Scaling ML Operations Like many organizations, the AI/ML team at Sense was finding it challenging to scale its ML operations.

ML 52

ML ML DataOps Data Scientist 52

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. He is very passionate about data-driven AI.

AWS 94

AWS ML ML Python 94

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. for GPT-4 with 5-shot prompting or 83.7% Operation Machine learning operations (MLOps) become more challenging for compound AI systems.

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. for GPT-4 with 5-shot prompting or 83.7% Operation Machine learning operations (MLOps) become more challenging for compound AI systems.

AI 40

AI AI DataOps Data Pipeline 40