article thumbnail

Application modernization overview

IBM Journey to AI blog

Generating configuration management inputs (for CMDB)and changing management inputs based on release notes generated from Agility tool work items completed per release are key Generative AI leverage areas. It also requires some focused effort to improve the data quality of data needed for tuning the models.

article thumbnail

Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Complete the following steps: Choose Prepare and analyze data.

professionals

Sign Up for our Newsletter

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

article thumbnail

Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

AWS Machine Learning Blog

We use this extracted dataset for exploratory data analysis and feature engineering. You can choose to sample the data from Snowflake in the SageMaker Data Wrangler UI. Another option is to download complete data for your ML model training use cases using SageMaker Data Wrangler processing jobs.

article thumbnail

Steve Salvin, Founder & CEO of Aiimi – Interview Series

Unite.AI

At Aiimi, we believe that AI should give users more, not less, control over their data. AI should be a driver of data quality and brand-new insights that genuinely help businesses make their most important decisions with confidence.

article thumbnail

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Can you see the complete model lineage with data/models/experiments used downstream? Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations.

article thumbnail

How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning Blog

Each business problem is different, each dataset is different, data volumes vary wildly from client to client, and data quality and often cardinality of a certain column (in the case of structured data) might play a significant role in the complexity of the feature engineering process.

article thumbnail

Best Large Language Models & Frameworks of 2023

AssemblyAI

It offers a simple API for applying LLMs to up to 100 hours of audio data, even exposing endpoints for common use tasks It's smart enough to auto-generate subtitles, identify speakers, and transcribe audio in real time. Start Building LLM Apps on Voice Data Ready to take action on your spoken data?