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 107

Data Warehouse Machine Learning Cost-Benefit Metadata 107

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

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. Generative AI and risky business There are some fundamental issues when using off-the-shelf, pre-built generative models.

Risk 65

Risk Deep Learning Metadata Modeling 65

O'Reilly on Data

JULY 28, 2020

You must detect when the model has become stale, and retrain it as necessary. Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment. Modeling and Evaluation.

Marketing 361

Marketing Experimentation Metrics Testing 361

Smart Data Collective

AUGUST 28, 2021

Data lakes are much more flexible as they can store raw data, including metadata, and schemas need to be applied only when extracting data. On the other hand, a data scientist may require access to unstructured data to detect patterns or build a deep learning model, which means that a data lake is a perfect fit for them.

Data Lake 139

Data Lake Data Warehouse Unstructured Data Structured Data 139

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

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 137

Machine Learning Data Science Data Lake Modeling 137

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. Model Drift.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 327

Management Machine Learning Experimentation Metrics 327

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. The first step in developing any model is gathering a suitable source of training data, and sentiment analysis is no exception. Sentiment analysis datasets.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

bridgei2i

DECEMBER 15, 2021

Naturally, the change in consumer behavior prompted media companies to change their business models. 1] These deep learning engines work by consolidating, comparing, and extracting information about the user’s historical data and then filtering out this information for suggestions or recommendations. billion by 2025.[1]

Recreation/Entertainment 52

Recreation/Entertainment Metadata Advertising Predictive Modeling 52

Smart Data Collective

OCTOBER 13, 2021

Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields). However, there will always be a decisive human factor, at least for a few decades yet. Virtualization goes beyond query federation.

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Visualization Cost-Benefit Big Data Prescriptive Analytics 135

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 102

Data Lake Unstructured Data Data Warehouse Data Quality 102

Ontotext

FEBRUARY 26, 2024

From a technological perspective, RED combines a sophisticated knowledge graph with large language models (LLM) for improved natural language processing (NLP), data integration, search and information discovery, built on top of the metaphactory platform. Let’s have a quick look under the bonnet.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Domino Data Lab

JUNE 26, 2019

Data science leaders want to know, at any given moment, not just how many data science projects are in flight but what the latest updates and roadblocks are when it comes to model development and what projects need their immediate attention. . They need help tracking models in production. They need help tracking projects.

Data Science 65

Data Science Dashboards Metadata Snapshot 65

IBM Big Data Hub

FEBRUARY 14, 2024

Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Cost-Benefit 83

Cost-Benefit Insurance Unstructured Data Machine Learning 83

Domino Data Lab

SEPTEMBER 9, 2019

We have configured the default Compute Environment in Domino to include all of the packages, libraries, models, and data you’ll need for this tutorial. That nlp variable is now your gateway to all things spaCy and loaded with the en_core_web_sm small model for English. metadata=convention_df["speaker"]? ).

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

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. Newer work in machine learning (e.g.,

Data Governance 84

Data Governance Machine Learning Metadata Big Data 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”. What can we learn from that? Key highlights from the session include. Transcript.

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Data Science Machine Learning Data Governance Modeling 109