article thumbnail

The Shift from Models to Compound AI Systems

BAIR

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. for GPT-4 with 5-shot prompting or 83.7%

AI 145
article thumbnail

How HR Tech Company Sense Scaled their ML Operations using Iguazio

Iguazio

Iguazio is an essential component in Sense’s MLOps and DataOps architecture, acting as the ML training and serving component of the pipeline. Establishing a deployment and monitoring strategy - Sense needed to create a sound deployment and monitoring strategy in a cost-effective and straightforward manner. Enabling quick experimentation.

ML 52
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

How Sense Uses Iguazio as a Key Component of Their ML Stack

Iguazio

Iguazio is an essential component in Sense’s MLOps and DataOps architecture, acting as the ML training and serving component of the pipeline. Establishing a deployment and monitoring strategy - Sense needed to create a sound deployment and monitoring strategy in a cost-effective and straightforward manner. Enabling quick experimentation.

ML 52
article thumbnail

Authoring custom transformations in Amazon SageMaker Data Wrangler using NLTK and SciPy

AWS Machine Learning Blog

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.

AWS 100
article thumbnail

The Shift from Models to Compound AI Systems

BAIR

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. for GPT-4 with 5-shot prompting or 83.7%

AI 40