Big Data, Deep Learning and Metadata

Big Data

Deep Learning

Metadata

Deep Learning Can Make a Difference

TDAN

AUGUST 6, 2019

Deep learning, as defined by MathWorks, is a system of artificial intelligence that is built around learning by example. Multiple industries have already understood the benefits that deep learning brings to their operational capabilities.

Deep Learning

Deep Learning Technology Data Architecture Metadata

Biggest Trends in Data Visualization Taking Shape in 2022

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. We would like to talk about data visualization and its role in the big data movement.

Visualization

Visualization Cost-Benefit Big Data Prescriptive Analytics

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Webinars

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

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Trending Sources

Analytics Vidhya

Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

MAY 21, 2019

Consider deep learning, a specific form of machine learning that resurfaced in 2011/2012 due to record-setting models in speech and computer vision. Machine learning is not only appearing in more products and systems, but as we noted in a previous post , ML will also change how applications themselves get built in the future.

Machine Learning

Machine Learning Technology Deep Learning Data Science

Webinars

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

MORE WEBINARS

Building a Beautiful Data Lakehouse

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake

Data Lake Unstructured Data Data Warehouse Data Quality

Themes and Conferences per Pacoid, Episode 13

Domino Data Lab

OCTOBER 9, 2019

Monica Youngman, director of data stewardship—check out her slides for the “Data Archiving at NOAA” keynote. Jonathan O’Neil, who’s led the spike on evaluating a large-scale migration to the cloud—check out his slides for the “The NOAA Big Data Project: By The Numbers” keynote. Metadata Challenges.

Deep Learning

Deep Learning Metadata Machine Learning Data Science

Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

AUGUST 28, 2021

In other words, data warehouses store historical data that has been pre-processed to fit a relational schema. Data lakes are much more flexible as they can store raw data, including metadata, and schemas need to be applied only when extracting data. Target User Group.

Data Lake

Data Lake Data Warehouse Unstructured Data Structured Data

NVIDIA RAPIDS in Cloudera Machine Learning

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. The raw data is in a series of CSV files. We will firstly convert this to parquet format as most data lakes exist as object stores full of parquet files. Introduction.

Machine Learning

Machine Learning Data Science Data Lake Modeling

Introducing watsonx: The future of AI for business

IBM Big Data Hub

MAY 9, 2023

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]

Data Warehouse

Data Warehouse Machine Learning Cost-Benefit Metadata

How to responsibly scale business-ready generative AI

IBM Big Data Hub

JUNE 26, 2023

ChatGPT was the first but today there are many competitors ChatGPT uses a deep learning architecture call the Transformer and represents a significant advancement in the field of NLP. Automatic capture of model metadata and facts provide audit support while driving transparent and explainable model outcomes.

Risk

Risk Deep Learning Metadata Modeling

The Cloud Connection: How Governance Supports Security

Alation

APRIL 14, 2022

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. The Cloud Data Migration Challenge. It’s More Important to Know What Your Data Means Than Where It Is.

Metadata

Metadata Data Governance Modeling Data-driven

The most valuable AI use cases for business

IBM Big Data Hub

FEBRUARY 14, 2024

The IBM team is even using generative AI to create synthetic data to build more robust and trustworthy AI models and to stand in for real-world data protected by privacy and copyright laws. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions.

Cost-Benefit

Cost-Benefit Insurance Unstructured Data Machine Learning

Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

APRIL 3, 2019

It includes perspectives about current issues, themes, vendors, and products for data governance. My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). We keep feeding the monster data. the flywheel effect.

Data Governance

Data Governance Machine Learning Metadata Big Data

Proposals for model vulnerability and security

O'Reilly on Data

MARCH 20, 2019

Watermarking is a term borrowed from the deep learning security literature that often refers to putting special pixels into an image to trigger a desired outcome from your model. It seems entirely possible to do the same with customer or transactional data. Watermark attacks. References and further reading.

Modeling

Modeling Machine Learning Predictive Modeling Consulting

Data Science, Past & Future

Domino Data Lab

JULY 22, 2019

By virtue of that, if you take those log files of customers interactions, you aggregate them, then you take that aggregated data, run machine learning models on them, you can produce data products that you feed back into your web apps, and then you get this kind of effect in business. That was the origin of big data.

Data Science

Data Science Machine Learning Data Governance Modeling

Data Leaders Brief

Deep Learning Can Make a Difference

Biggest Trends in Data Visualization Taking Shape in 2022

Webinars

Trending Sources

Becoming a machine learning company means investing in foundational technologies

Webinars

Building a Beautiful Data Lakehouse

Themes and Conferences per Pacoid, Episode 13

Understanding the Differences Between Data Lakes and Data Warehouses

NVIDIA RAPIDS in Cloudera Machine Learning

Introducing watsonx: The future of AI for business

How to responsibly scale business-ready generative AI

The Cloud Connection: How Governance Supports Security

The most valuable AI use cases for business

Themes and Conferences per Pacoid, Episode 8

Proposals for model vulnerability and security

Data Science, Past & Future

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