IBM Big Data Hub

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Cost-Benefit 91

Cost-Benefit Insurance Unstructured Data Machine Learning 91

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. As a machine learning problem, it is a classification task with tabular data, a perfect fit for RAPIDS. Introduction. See < [link] > for more details.

Machine Learning 136

Machine Learning Data Science Data Lake Modeling 136

Domino Data Lab

APRIL 3, 2019

That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Plus, the more mature machine learning (ML) practices place greater emphasis on these kinds of solutions than the less experienced organizations. We keep feeding the monster data.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Insight

MAY 16, 2019

It is a high-level, multifaceted field that allows machines to iteratively learn and understand complex representations from images and videos to automate human visual tasks. How Deep Learning scales based on the amount of Data [Copyright: Andrew Ng ]. Python-Labellmg interface.

Deep Learning 84

Deep Learning Machine Learning Metadata Data Science 84

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”. But for most enterprise, using machine learning…not really.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

DataRobot Blog

MARCH 9, 2022

With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. Whenever you test a machine learning method, it’s helpful to have a baseline method and accuracy level against which to measure improvements. It provides 1.6

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

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. Data poisoning attacks. Watermark attacks.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Metadata and artifacts needed for a full audit trail.

Modeling 194

Modeling Machine Learning Testing Metrics 194

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. One of the longer-term trends that we’re seeing with Airflow , and so on, is to externalize graph-based metadata and leverage it beyond the lifecycle of a single SQL query, making our workflows smarter and more robust. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Ontotext

MAY 22, 2023

This is the case with the so-called intelligent data processing (IDP), which uses a previous generation of machine learning. We use other deep learning techniques for such tasks. Atanas Kiryakov : Technically, the data fabric paradigm employs rich metadata to streamline data discovery, integration and preparation.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

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 109

Data Warehouse Cost-Benefit Machine Learning Modeling 109

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

Domino Data Lab

OCTOBER 9, 2019

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. The metadata must be converted into both W3C and ISO standards for publication. Check out the NOAA data catalog for examples.

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. . On the other hand, they don’t support transactions or enforce data quality.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

Ontotext

FEBRUARY 26, 2024

Quality assurance process, covering gold standard creation , extraction quality monitoring, measurement, and reporting via Ontotext Metadata Studio. Using machine learning, RED indicates the impact of events on stock prices. Semantic model management – events schema and instance management as well as taxonomical alignment.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

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

Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. metadata=convention_df["speaker"]? ). That said, spaCy has been quick to incorporate the SOTA advances in machine learning, effectively becoming a conduit for moving research into industry.

Deep Learning 110

Deep Learning Machine Learning Visualization Data Science 110