Cloudera

SEPTEMBER 20, 2022

The state of the art in AI systems for artistic tasks almost universally use deep-learning models, which presuppose a significant amount of compute resources both to create them, and once created to continue to use them for producing images. The content of these data sets may introduce problematic bias into a model’s results.

Deep Learning 85

Deep Learning Cost-Benefit Risk Data-driven 85

Smart Data Collective

MARCH 27, 2023

billion on AI in 2021 , but small businesses may spend even more on AI-driven financial management software. Some of the benefits of AI in banking include: Banks use AI bots to onboard clients and analyze borrower risk. They have also started integrated computer vision and deep learning technology to identify inefficiencies.

Software 88

Software Finance Cost-Benefit Data-driven 88

CIO Business Intelligence

MARCH 30, 2022

But that takes a deep understanding of the decision-making process, the risks and rewards of each decision, the acceptable margin of error, and the ability to figure how confident you should be in any decision offered by your automated decision processes. A certain amount of learning is always business as usual.

Machine Learning 144

Machine Learning Testing Risk Data-driven 144

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

CIO Business Intelligence

APRIL 15, 2022

Derek Driggs, a machine learning researcher at the University of Cambridge, together with his colleagues, published a paper in Nature Machine Intelligence that explored the use of deep learning models for diagnosing the virus. The algorithm learned to identify children, not high-risk patients.

Analytics 144

Analytics Machine Learning Insurance Testing 144

DataRobot Blog

FEBRUARY 2, 2023

Large language models, also known as foundation models, have gained significant traction in the field of machine learning. These models are pre-trained on large datasets, which allows them to perform well on a variety of tasks without requiring as much training data. What Are Large Language Models?

Modeling 52

Modeling Machine Learning Data Processing Interactive 52

CIO Business Intelligence

MARCH 22, 2023

Enter the new class ML data scientists require large quantities of data to train machine learning models. Then the trained models become consumers of vast amounts of data to gain insights to inform business decisions. In the training phase, the primary objective is to use existing examples to train a model.

Machine Learning 102

Machine Learning Cost-Benefit Modeling Risk 102

O'Reilly on Data

JANUARY 25, 2021

Observability” risks becoming the new name for monitoring. Models are neither code nor data, and we don’t have adequate tools for versioning models either (though tools like MLflow are a start). Frequent deployment assumes that the software can be built relatively quickly, but training a model can take days.

Machine Learning 363

Machine Learning Software Testing Technology 363