O'Reilly on Data

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. You see these drivers involving risk and cost, but also opportunity. What can we learn from that?

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Ontotext

FEBRUARY 26, 2024

The answers to these foundational questions help you uncover opportunities and detect risks. Further, RED’s underlying model can be visually extended and customized to complex extraction and classification tasks. Why do risk and opportunity events matter? , and “What is the financial impact?”.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

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. RAPIDS brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building. Introduction.

Machine Learning 136

Machine Learning Data Science Data Lake Modeling 136

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. The answer is that generative AI leverages recent advances in foundation models. Watsonx.ai

Data Warehouse 109

Data Warehouse Cost-Benefit Machine Learning Modeling 109

Ontotext

MAY 22, 2023

What is the future of knowledge graphs in the era of ChatGPT and Large Language Models? To start with, Large Language Models (LLM) will not replace databases. They are good for compressing information, but one cannot retrieve from such a model the same information that it got trained on. That’s something that LLMs cannot do.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

Alation

APRIL 14, 2022

And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. 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. Cloud governance.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. Using ML models to search more effectively brought the search space down to 102—which can run on modest hardware. Introduction. That’s impressive.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

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 104

Data Lake Unstructured Data Data Warehouse Data Quality 104

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238