Precisely

FEBRUARY 10, 2023

Advanced analytics and AI/ML continue to be hot data trends in 2023. Read our Report Improving Data Integrity and Trust through Transparency and Enrichment Data trends for 2023 point to the need for enterprises to govern and manage data at scale, using automation and AI/ML technology.

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Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. ML and DataOps teams). At one level, it makes sense – there is certainly a lot of interest in DataOps today. Thus was born the data catalog. data pipelines) to support.

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AWS Machine Learning Blog

APRIL 17, 2023

In other words, companies need to move from a model-centric approach to a data-centric approach.” – Andrew Ng A data-centric AI approach involves building AI systems with quality data involving data preparation and feature engineering. AWS AI services easily integrate with your applications to address many common use cases.

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ODSC - Open Data Science

JULY 20, 2023

Popular Machine Learning Libraries, Ethical Interactions Between Humans and AI, and 10 AI Startups in APAC to Follow Demystifying Machine Learning: Popular ML Libraries and Tools In this comprehensive guide, we will demystify machine learning, breaking it down into digestible concepts for beginners, including some popular ML libraries to use.

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DataRobot Blog

SEPTEMBER 13, 2022

They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value. What data scientists do is directly tied to an organization’s AI maturity level. Driving Innovation with AI: Getting Ahead with DataOps and MLOps.

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Precisely

AUGUST 3, 2023

For example, workflow automation (43%), AI/ ML (41%), and DataOps (31%) are complementary technologies that enable organizations to automate data management and data processes so they can improve data quality even when people and skills are scarce.

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Precisely

FEBRUARY 9, 2024

In 2023, organizations dealt with more data than ever and witnessed a surge in demand for artificial intelligence use cases – particularly driven by generative AI. It’s easy to see why there’s been an astronomical rise in the usage of generative AI over the past year for consumers and business users alike.

Data Quality 69

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Alation

JANUARY 25, 2022

This “analysis” is made possible in large part through machine learning (ML); the patterns and connections ML detects are then served to the data catalog (and other tools), which these tools leverage to make people- and machine-facing recommendations about data management and data integrations.

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IBM Journey to AI blog

SEPTEMBER 5, 2024

Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality. To maximize the value of their AI initiatives, organizations must maintain data integrity throughout its lifecycle.

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BAIR

FEBRUARY 18, 2024

This is enforced with the `more` excerpt separator. --> AI caught everyone’s attention in 2023 with Large Language Models (LLMs) that can be instructed to perform general tasks, such as translation or coding, just by prompting. In this post, we analyze the trend toward compound AI systems and what it means for AI developers.

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Alation

APRIL 4, 2023

Troubleshooting data issues , for an exploding number of disjointed systems and tools, breaks self-service for data users and creates gaps in visibility for dataOps. Building data pipelines is challenging, and complex requirements (as well as the separation of many sources) leads to a lack of trust.

DataOps 52

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Alation

SEPTEMBER 14, 2021

Stewardship Workbench: This feature uses AI and ML to automate the discovery of candidate data stewards based on who is actually using data. Given this, data governance should be a key enabler of DataOps. It improves governance processes and makes DataOps processes less constraining upon organizations.

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Precisely

SEPTEMBER 18, 2023

Data observability is a key element of data operations (DataOps). It enables a big-picture understanding of the health of your organization’s data through continuous AI/ML-enabled monitoring – detecting anomalies throughout the data pipeline and preventing data downtime. Why is data observability so important?

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DataRobot Blog

JUNE 27, 2022

Monitoring Modern Machine Learning (ML) Methods In Production. Given the numerous variables that may change, how does the financial institution develop a robust monitoring strategy, and apply them in the context of ML models? Driving Innovation with AI: Getting Ahead with DataOps and MLOps. Monitoring Model Metrics.

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ML ML Machine Learning Machine Learning 59

Precisely

DECEMBER 12, 2023

Data observability is a foundational element of data operations (DataOps). The best data observability tools incorporate AI to identify and prioritize potential issues. For a data scientist building machine learning models for an important AI initiative, data observability helps set the stage for long-term success.

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Snorkel AI

OCTOBER 31, 2023

government to address the safety, transparency, and use of AI in the near future. As AI takes a larger role in all of our lives, those are important concerns to consider. They also represent difficult challenges that require an acute focus on data that is used to train the AI itself and how it is prepared, managed, and applied.

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Snorkel AI

OCTOBER 31, 2023

government to address the safety, transparency, and use of AI in the near future. As AI takes a larger role in all of our lives, those are important concerns to consider. They also represent difficult challenges that require an acute focus on data that is used to train the AI itself and how it is prepared, managed, and applied.

AI 103

AI AI DataOps Data Governance 103

Snorkel AI

OCTOBER 31, 2023

government to address the safety, transparency, and use of AI in the near future. As AI takes a larger role in all of our lives, those are important concerns to consider. They also represent difficult challenges that require an acute focus on data that is used to train the AI itself and how it is prepared, managed, and applied.

AI 52

AI AI DataOps Data Governance 52