Domino Data Lab

JULY 17, 2021

The system here will identify, via some meaningful sense, which existing speakers’ model does the utterance match. If the unknown utterance is spoken by a speaker outside the list of existing speakers, the model will nonetheless map it to some speaker from that list. It determines whether the individual is who they claim to be.

Modeling 64

Modeling Testing Machine Learning Measurement 64

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.

Risk 52

Risk Modeling Metrics Business Objectives 52

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Dataiku

MARCH 25, 2024

This scenario is not science fiction but a glimpse into the capabilities of Multimodal Large Language Models (M-LLMs), where the convergence of various modalities extends the landscape of AI. But instead, a machine seamlessly identifies the scene and its location, provides a detailed description, and even suggests nearby attractions.

Visualization 132

Visualization Modeling Experimentation Testing 132

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task.

Enterprise 118

Enterprise Modeling Testing Data-driven 118

IBM Big Data Hub

SEPTEMBER 19, 2023

Generative AI for coding is possible because of recent breakthroughs in large language model (LLM) technologies and natural language processing (NLP). It uses deep learning algorithms and large neural networks trained on vast datasets of diverse existing source code. How does generative AI code generation work?

Software 81

Software IT Visualization Deep Learning 81

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 81

Metrics Machine Learning Statistics Risk 81

IBM Big Data Hub

AUGUST 11, 2023

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. These insights can help drive decisions in business, and advance the design and testing of applications. How the models are stored.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

IBM Big Data Hub

JULY 11, 2023

Implementing AI can help recognize unusual or suspicious patterns in insurance claims, such as billing for costly services or procedures not performed, unbundling (which is billing for the individual steps of a procedure as though they were separate procedures), and performing unnecessary tests to take advantage of insurance payments.

Deep Learning 55

Deep Learning Cost-Benefit Insurance Machine Learning 55

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

CIO Business Intelligence

APRIL 24, 2023

Generative AI (Artificial Intelligence) and its underlying foundation models represent a paradigm shift in innovation, significantly impacting enterprises exploring AI applications. These foundation models are trained on vast amounts of data. The model can then predict or classify new, previously unseen data.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

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. After cleaning, the data is now ready for processing.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ). Training domain-specific models.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

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

Smart Data Collective

FEBRUARY 20, 2022

With these tools, your SaaS can: Merge and improve the application code constantly Automate the development, testing, and release of software Integrate operations and developer workflows And much more. AWS also offers a variety of AI model development and delivery platforms , as well as packaged AI-based applications. Conclusions.

Cost-Benefit 114

Cost-Benefit Data Lake Software Machine Learning 114

Domino Data Lab

OCTOBER 7, 2020

MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. In this blog we show what the changes in behavior of data are in high dimensions. The accuracy of any predictive model approaches 100%.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

CIO Business Intelligence

JUNE 2, 2022

The system, which develops in-depth 3D subsoil models to see up to 15 kilometers underground, led to the discovery of Zohr , the largest known natural gas field in the Mediterranean. For optimizing existing resources, Eni uses HPC5 to model, study, and ultimately improve refinement operations. .

Informatics 98

Informatics Modeling Testing Technology 98

Smarten

JUNE 14, 2023

GPT, or Generative Pre-Trained Transformer, is a Large Language Model (LLM). Privacy, Security and Standards The current GPT environment has not been thoroughly tested and standardized to provide appropriate multi-layered security and privacy and to meet government and industry standards.

Optimization 52

Optimization IT Digital Transformation Consulting 52

Insight

OCTOBER 25, 2019

Image credit: [link] As the title suggests, this is a story about a question that may resonate well with many machine learning practitioners trying to build applications in the real world, where clean and annotated data on a specific problem can be sparse— How do we leverage the power of AI when we have very little data? not just images?—?unseen

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

IBM Big Data Hub

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post will clarify some of the ambiguity. Machine learning is a subset of AI.

Deep Learning 57

Deep Learning Machine Learning Unstructured Data Modeling 57

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. DL models can improve over time through further training and exposure to more data. These technologies enable systems to interact, learn from interactions, adapt and become more efficient.

Enterprise 96

Enterprise Interactive Data-driven Key Performance Indicator 96

Cloudera

MARCH 13, 2019

A Data Scientist : Organizations who show how they improved analytics, delivered new actionable intelligence, or designed systems for distributed deep learning and artificial intelligence to the organization’s business and customers. appeared first on Cloudera Blog. The post Introducing the 2019 Data Heroes – EMEA!

Data Architecture 70

Data Architecture Deep Learning Machine Learning Big Data 70

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

IBM Big Data Hub

OCTOBER 16, 2023

Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions. Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection. Spotify uses ML models to generate its song recommendations.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

Insight

MAY 7, 2019

Services like Clarifai , and Google AutoML have made it very easy to create image classification models with less labeled data, but it’s not as easy to create such models for text classification. Transfer learning has simplified image classification tasks. fastText, word embedding, and language models?—?in fastText? —?The

Modeling 75

Modeling Testing Machine Learning Interactive 75

Domino Data Lab

JULY 28, 2021

In its current form , data science is limitless in terms of industry and application; the only requirement is data, compute power, data scientists, and a desire to become model-driven. Only when the business understands the models will they trust them to drive decisions in the business. None of these techniques are new.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Insight

APRIL 9, 2019

NASA [Public domain] In this blog, I’ll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. The biggest categories of cost for hardware designers and manufacturers are testing, verification, and calibration of their control systems.

Manufacturing 75

Manufacturing Machine Learning Testing Software 75

Domino Data Lab

AUGUST 9, 2021

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. What Are Modeling Tools? Importance of Modeling Tools. Types of Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

CDW Research Hub

JULY 31, 2019

Recent advances in machine learning, and more specifically its subset, deep learning, have made it possible for computers to better understand natural language. These deep learning models can analyze large volumes of text and provide things like text summarization, language translation, context modeling, and sentiment analysis.

Deep Learning 49

Deep Learning Machine Learning Testing Modeling 49

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. Testing and Data Observability. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Testing and Data Observability.

Testing 300

Testing Machine Learning Consulting Data Quality 300

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.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Insight

JANUARY 14, 2020

a “pre-trained” model) to be recycled and reused for many different tasks. Keras is an open source deep learning API that was written in Python and runs on top of Tensorflow, so it’s a little more user-friendly and high-level than Tensorflow. A vanilla NN can only detect features that are in identical positions within the image.

Metrics 58

Metrics Optimization Modeling Testing 58

Cloudera

SEPTEMBER 11, 2020

Due to COVID-19, retailers world-wide had their e-commerce capabilities put to the test with entire countries being in lockdown for weeks, even months, due to the pandemic. . What they have learned is that often their legacy Machine Learning models (e.g. Data and analytics.

Manufacturing 93

Manufacturing Forecasting Digital Transformation Sales 93

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.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Cloudera

OCTOBER 17, 2017

Deep learning is in the news. But deep learning is a tool that enterprises use to solve practical problems. In this blog, we provide a few examples that show how organizations put deep learning to work. In this blog, we provide a few examples that show how organizations put deep learning to work.

Deep Learning 47

Deep Learning Data Science Machine Learning Modeling 47

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. We covered different ways of measuring model efficiency and showed ways to visualize this and select models based on it.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

IBM Big Data Hub

AUGUST 1, 2023

True to their name, generative AI models generate text, images, code , or other responses based on a user’s prompt. But what makes the generative functionality of these models—and, ultimately, their benefits to the organization—possible? That’s where the foundation model enters the picture.

Modeling 55

Modeling Cost-Benefit Data Lake Machine Learning 55