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. In this tutorial, we will illustrate how RAPIDS can be used to tackle the Kaggle Home Credit Default Risk challenge. Introduction. The raw data is in a series of CSV files.

Machine Learning 136

Machine Learning Data Science Data Lake Modeling 136

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. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Ontotext

FEBRUARY 26, 2024

The answers to these foundational questions help you uncover opportunities and detect risks. We bundle these events under the collective term “Risk and Opportunity Events” This post is part of Ontotext’s AI-in-Action initiative aimed to empower data, scientists, architects and engineers to leverage LLMs and other AI models.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Ontotext

MAY 22, 2023

We use other deep learning techniques for such tasks. Atanas Kiryakov : A CMS typically contains modest metadata , describing the content: date, author, few keywords and one category from a taxonomy. Knowledge graphs are all about using semantic metadata to serve these purposes. That’s something that LLMs cannot do.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Watermark attacks. Newer types of fair and private models (e.g.,

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

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. 1] Users can access data through a single point of entry, with a shared metadata layer across clouds and on-premises environments.

Data Warehouse 108

Data Warehouse Cost-Benefit Machine Learning Modeling 108

O'Reilly on Data

JUNE 19, 2019

Metadata and artifacts needed for a full audit trail. Related to this is the need to monitor bias, locality effects, and related risks. Related content : “Modern Deep Learning: Tools and Techniques” - a new tutorial at the Artificial Intelligence conference in San Jose. Deep automation in machine learning”. “We

Modeling 194

Modeling Machine Learning Testing Metrics 194

Domino Data Lab

JULY 22, 2019

and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries. What I’m trying to say is this evolution of system architecture, the hardware driving the software layers, and also, the whole landscape with regard to threats and risks, it changes things.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

OCTOBER 9, 2019

In some cases, their work is vital when millions of lives may be at risk due to natural disasters. Metadata Challenges. NOAA teams face substantial challenges in terms of metadata exchange: much of their incoming raw sensor telemetry is time-series data, which uses a netCDF TF format natively. How cool is that?!

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

CIO Business Intelligence

MARCH 9, 2022

If those in charge of managing the data lake don’t create precise processes and metadata for organizing data, the lake can quickly devolve into what’s come to be known as a “data swamp”—a data lake that makes it hard for users to locate data. . Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

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 238

Machine Learning Technology Deep Learning Data Science 238