Data Processing Data Quality Document Modeling 
datapine
SEPTEMBER 29, 2022
1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.
O'Reilly on Data
DECEMBER 12, 2019
Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]
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DataKitchen
FEBRUARY 21, 2023
Overview of Gartner’s data engineering enhancements article To set the stage for Gartner’s recommendations, let’s give an example of a new Data Engineering Manager, Marcus, who faces a whole host of challenges to succeed in his new role: Marcus has a problem. Summary: 10x your data engineering game.
FineReport
APRIL 26, 2023
For example, a computer manufacturing company could develop new models or add features to products that are in high demand. E-commerce giants like Alibaba and Amazon extensively use big data to understand the market. Unlike traditional databases, processing large data volumes can be quite challenging.
Domino Data Lab
OCTOBER 1, 2020
Combining the power of Domino Data Labs with Okera, your data scientists only get access to the columns, rows, and cells allowed, easily removing or redacting sensitive data such as PII and PHI not relevant to training models. So what does this look like? client('s3') obj = s3.get_object(Bucket='clinical-trials',
AWS Big Data
NOVEMBER 16, 2023
As with all AWS services, Amazon Redshift is a customer-obsessed service that recognizes there isn’t a one-size-fits-all for customers when it comes to data models, which is why Amazon Redshift supports multiple data models such as Star Schemas, Snowflake Schemas and Data Vault.
IBM Big Data Hub
JANUARY 9, 2024
Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.
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