IBM Big Data Hub

JUNE 14, 2023

Traditional AI tools, especially deep learning-based ones, require huge amounts of effort to use. And then you need highly specialized, expensive and difficult to find skills to work the magic of training an AI model. But that’s all changing thanks to pre-trained, open source foundation models.

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Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. The process of creating models is called modeling.

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The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

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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?

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DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

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Cloudera

JULY 27, 2021

Fitting Prophet models with complex seasonalities for electricity demand forecasting. The entirely custom front-end to one of our prototype applications with a probabilistic model of NPC real estate. There are many uses for interactive applications in the machine learning development lifecycle.

Data Science 115

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Cloudera

OCTOBER 24, 2023

The AI technologies of today—including not just large language models (LLMs) but also deep learning, reinforcement learning, and natural-language processing (NLP) tools—will equip telcos with powerful new automation and analytics capabilities.

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IBM Big Data Hub

FEBRUARY 23, 2024

The emergence of NLG has dramatically improved the quality of automated customer service tools, making interactions more pleasant for users, and reducing reliance on human agents for routine inquiries. Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. billion by 2030.

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IBM Big Data Hub

FEBRUARY 13, 2024

The compact design and touch-based interactivity seemed like a leap into the future. Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” Remember how cool it felt when you first held a smartphone in your hand?

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IBM Big Data Hub

OCTOBER 12, 2023

Early iterations of the AI applications we interact with most today were built on traditional machine learning models. These models rely on learning algorithms that are developed and maintained by data scientists. This ability allows AGI to learn and perform any intellectual task that a human being can.

Machine Learning 69

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IBM Big Data Hub

DECEMBER 15, 2023

When AI algorithms, pre-trained models, and data sets are available for public use and experimentation, creative AI applications emerge as a community of volunteer enthusiasts builds upon existing work and accelerates the development of practical AI solutions.

Deep Learning 65

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Cloudera

OCTOBER 26, 2022

Deep Learning for Anomaly Detection : ?? Apply modern, deep learning techniques for anomaly detection to identify network intrusions. Deep Learning for Image Analysis : Build a semantic search application with deep learning models. AMPs so far include: . AMP hackathon.

Machine Learning 76

Machine Learning Deep Learning Cost-Benefit Forecasting 76

IBM Big Data Hub

MAY 17, 2024

Poorly run implementations of traditional or generative AI in commerce—such as models trained on inadequate or inappropriate data—lead to bad experiences that alienate consumers and businesses. To take one example, AI-facilitated tools like voice navigation promise to upend the way users fundamentally interact with a system.

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O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. On the machine learning side, we are entering what Andrei Karpathy, director of AI at Tesla, dubs the Software 2.0

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IBM Big Data Hub

DECEMBER 5, 2023

With the rise of highly personalized online shopping, direct-to-consumer models, and delivery services, generative AI can help retailers further unlock a host of benefits that can improve customer care, talent transformation and the performance of their applications.

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Unstructured Data Cost-Benefit Experimentation Machine Learning 91

Cloudera

NOVEMBER 3, 2021

Thanks to pioneers like Andrew NG and Fei-Fei Li, GPUs have made headlines for performing particularly well with deep learning techniques. Today, deep learning and GPUs are practically synonymous. While deep learning is an excellent use of the processing power of a graphics card, it is not the only use.

Deep Learning 117

Deep Learning Machine Learning Data Science Cost-Benefit 117

IBM Big Data Hub

OCTOBER 18, 2023

. “Artificial Intelligence Fundamentals” introduces learners to the history of AI and explores the ways that AI makes predictions, understands language and images, and learns using Deep Learning , neural networks that attempt to simulate the human brain.

Machine Learning 77

Machine Learning Deep Learning Technology Marketing 77

Laminar Security

DECEMBER 13, 2023

As we generate and interact with unprecedented volumes of data, the task of accurately identifying, categorizing, and utilizing this information becomes increasingly difficult. ML, a subset of AI, involves training models on existing data sets so they can make predictions or decisions without being explicitly programmed to do so.

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Cloudera

FEBRUARY 25, 2021

AMPs move the starting line for any ML project by enabling data scientists to start with a full end-to-end project developed for a similar use case, including a trained and deployed ML model, as well as prebuilt predictive business applications, out of the box. The work of a machine learning model developer is highly complex.

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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

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Data Warehouse Machine Learning Cost-Benefit Metadata 107

Smarten

APRIL 19, 2024

OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). GPT – Is based on a Large Language Model (LLM). Benefits include customized and optimized models, data, parameters and tuning. Open AI was developed by Microsoft.

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Analytics Predictive Modeling KPI Machine Learning 57

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

Machine Learning 75

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IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

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 paper determined the technique not fit for clinical use.

Analytics 140

Analytics Machine Learning Insurance Testing 140

Domino Data Lab

JULY 15, 2021

These data science tools are used for doing such things as accessing, cleaning and transforming data, exploratory analysis, creating models, monitoring models and embedding them in external systems. They save data scientists a great deal of time by eliminating the need to write code from scratch every time they build a model.

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IBM Big Data Hub

JULY 11, 2023

This sort of routine monitoring and scheduling takes tasks off the hands of clinical staff, who can then spend more time directly on patient care, where human judgment and interaction matter most. In some instances, such as identifying cardiomegaly in chest X-rays, they found that a hybrid human-AI model produced the best results.

Deep Learning 55

Deep Learning Cost-Benefit Insurance Machine Learning 55

IBM Big Data Hub

JANUARY 17, 2024

A chatbot is a program or script designed to interact and respond to humans in real-time conversation. AI-powered bots leverage machine learning and NLP ( natural language processing ) to understand prompts and context. They can learn from past interactions and improve over time.

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Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal. Computer Science Skills.

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Data Science Statistics Recreation/Entertainment Predictive Analytics 110

Cloudera

NOVEMBER 19, 2020

Machine learning algorithms enable fraud detection systems to distinguish between legitimate and fraudulent behaviors. Some of these algorithms can be adaptive to quickly update the model to take into account new, previously unseen fraud tactics allowing for dynamic rule adjustment. The modeling process begins with data collection.

Machine Learning 117

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IBM Big Data Hub

SEPTEMBER 5, 2023

Many of us have interacted with these chatbots or virtual assistants on our phones or through devices in our homes—such as Apple’s Siri, Amazon Alexa and Google Assistant. You may have interacted with these chatbots via SMS text messaging, social media or with messenger applications in the workplace.

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IBM Big Data Hub

AUGUST 15, 2023

To overcome these challenges will require a shift in many of the processes and models that businesses use today: changes in IT architecture, data management and culture. It ensures the data and AI models are not only accurate, providing a higher-quality outcome, but that the data is being used in a safe and ethical way.

IT 75

IT Data Governance Modeling Cost-Benefit 75

IBM Big Data Hub

JULY 17, 2023

Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. is a studio to train, validate, tune and deploy machine learning (ML) and foundation models for Generative AI. Watsonx.ai

Consulting 54

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datapine

AUGUST 14, 2019

Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning. offers many statistics and machine learning abilities.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Sisense

APRIL 15, 2021

AutoML’s aim is to automate the development of machine learning models. In broad strokes, these tasks include: Data preparation Feature engineering Model generation Model evaluation. In other words, data experts can dovetail their coding skills with AI functionality to produce more sophisticated and more accurate models.

Analytics 98

Analytics Data Science Modeling Machine Learning 98

Ontotext

DECEMBER 15, 2023

It includes only ML papers and related entities; this SPARQL query shows some statistics: papers tasks models datasets methods evaluations repos 376557 4267 24598 8322 2101 52519 153476 We can start with these repositories (most of them are on Github) and get all their topics. We can start with a connecting dataset like LinkedPapersWithCode.

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Machine Learning Interactive Deep Learning Statistics 69

Cloudera

NOVEMBER 15, 2021

This blog discusses quantifications, types, and implications of data. In other words, structured data has a pre-defined data model , whereas unstructured data doesn’t. . It facilitates AI because, to be useful, many AI models require large amounts of data for training. Enter the field of learning with limited labeled data.

Unstructured Data 88

Unstructured Data Recreation/Entertainment Structured Data Reporting 88

Cloudera

AUGUST 20, 2020

UOB used deep learning to improve detection of procurement fraud, thereby fighting financial crime. The post Data for Enterprise AI: at the very forefront of innovation appeared first on Cloudera Blog. This has helped UOB to deliver more personalised features and services as part of the TMRW digital banking application. .

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