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Data Quality in Machine Learning

Pickl AI

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning? Bias in data can result in unfair and discriminatory outcomes.

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

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8 Best Programming Language for Data Science

Pickl AI

This “write once, run anywhere” capability allows developers to create applications that are not tied to a specific operating system, increasing portability and flexibility. Key Features of Scala Data Integration and Management: SAS provides robust tools for data integration, cleansing, and transformation.

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Top Synthetic Data Tools/Startups For Machine Learning Models in 2023

Marktechpost

YData By enhancing the caliber of training datasets, YData offers a data-centric platform that speeds up the creation and raises the return on investment of AI solutions. Data scientists can now enhance datasets using cutting-edge synthetic data generation and automated data quality profiling.

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Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark

AWS Machine Learning Blog

The benefits of this solution are: You can flexibly achieve data cleaning, sanitizing, and data quality management in addition to chunking and embedding. You can build and manage an incremental data pipeline to update embeddings on Vectorstore at scale. Savio Dsouza is a Software Development Manager on the AWS Glue team.

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Archana Joshi, Head – Strategy (BFS and EnterpriseAI), LTIMindtree – Interview Series

Unite.AI

Next, technical interventions are incorporated into our internal processes that focus on high-quality, unbiased data, with measures to ensure data integrity and fairness. Integrating GenAI into Agile practices is transforming how teams work. Our platform is built around the principles of responsible and mindful AI.

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Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

AWS Machine Learning Blog

Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.