CIO Business Intelligence

JANUARY 24, 2024

Generative AI and large language models (LLMs) like ChatGPT are only one aspect of AI. It’s the culmination of a decade of work on deep learning AI. Model sizes: ~5 billion to >1 trillion parameters. Model sizes: ~Millions to billions of parameters. AI encompasses many things.

Deep Learning 92

Deep Learning Unstructured Data Machine Learning Modeling 92

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

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

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

KDnuggets

OCTOBER 16, 2019

This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.

Metrics 53

Metrics Deep Learning Machine Learning Modeling 53

Analytics Vidhya

SEPTEMBER 10, 2021

This article was published as a part of the Data Science Blogathon Introduction With ignite, you can write loops to train the network in just a few lines, add standard metrics calculation out of the box, save the model, etc. Well, for those who have moved from TF to PyTorch, we can say that the ignite […].

Testing 275

Testing Metrics Data Science Publishing 275

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

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 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data).

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

Domino Data Lab

APRIL 29, 2021

Deep learning enthusiasts are increasingly putting NVIDIA’s GTC at the top of their gotta-be-there conference list. Three of them were particularly compelling and inspired a new point of view on transfer learning that I feel is important for analytical practitioners and leaders to understand. Deep Learning Trends from GTC21.

Data Science 134

Data Science Deep Learning Modeling Forecasting 134

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 83

Metrics Machine Learning Risk Statistics 83

DataRobot

DECEMBER 20, 2021

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. With frameworks like Tensorflow , Keras , Pytorch, etc.,

Visualization 97

Visualization Deep Learning Machine Learning Modeling 97

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

CIO Business Intelligence

JUNE 21, 2022

federal government, HPC is being used to accelerate basic science, develop therapeutics and other treatments for COVID-19, perform military applications such as simulations, handle climate and weather modeling, and a myriad of other tasks in diverse agencies. . Within the U.S. HPC Growth in U.S. Government. For years, U.S. In the U.S.,

Deep Learning 102

Deep Learning Big Data ROI Software 102

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

Smart Data Collective

JULY 1, 2023

Image annotation is the act of labeling images for AI and machine learning models. This helps train the AI model by assigning classes to different entities in an image. The resulting structured data is then used to train a machine learning algorithm. Consistency and agreement Establish an agreement metric (e.g.,

Metrics 64

Metrics Machine Learning Uncertainty Deep Learning 64

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. A complete DataOps program will have a unified, system-wide view of process metrics using a common data store. Datatron — Automates deployment and monitoring of AI models.

Testing 300

Testing Machine Learning Consulting Data Quality 300

Rocket-Powered Data Science

DECEMBER 15, 2018

Many unsupervised learning models can converge more readily and be more valuable if we know in advance which parameterizations are best to choose. If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data.

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

Sisense

APRIL 10, 2020

These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. Some standard Python libraries are Pandas, Numpy, Scikit-Learn, SciPy, and Matplotlib. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

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.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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.

Deep Learning 83

Deep Learning Machine Learning Forecasting Visualization 83

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

O'Reilly on Data

APRIL 19, 2021

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%).

Enterprise 353

Enterprise Modeling Risk Manufacturing 353

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

CIO Business Intelligence

JUNE 1, 2022

The company is applying winning insights from rapid, data-driven, evolutionary models versus relying on engine speed and aerodynamics alone to win races. which are virtual models of objects, systems, or processes ? and artificial intelligence (AI) and machine learning (ML) technologies. . Just starting out with analytics?

Manufacturing 108

Manufacturing Digital Transformation Predictive Analytics Strategy 108

IBM Big Data Hub

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How the models are stored.

Data Science 56

Data Science Machine Learning Cost-Benefit Deep Learning 56

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 56

Machine Learning Data Science Statistics Deep Learning 56

Smart Data Collective

FEBRUARY 14, 2021

New SaaS businesses have discovered that data analytics is important for facilitating many aspects of their models. Outline Your Product with Deep Learning Modeling. Deep learning tools can make it easier to model these products. Many design tools have helped with design modeling for years.

Data-driven 85

Data-driven Big Data Deep Learning Software 85

DataRobot

OCTOBER 20, 2021

It is a key component of developing responsible AI, and promotes trust not only in AI concepts but also in individual AI models. rule-based AI , machine learning , deep learning , etc.) What Is Data Literacy? The distinction between various types of AI systems: Distinguishing between various technologies (i.e.,

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

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 100

Enterprise Interactive Data-driven Key Performance Indicator 100

DataRobot Blog

FEBRUARY 14, 2022

It can be difficult to convince stakeholders at your organization to trust a model, even if it’s 90% accurate, if it’s unclear how a model arrives at decisions. Explainability requirements continue after the model has been deployed and is making predictions. Some models have “built-in” features that make them easy to understand.

Machine Learning 52

Machine Learning Metrics Sales Modeling 52

DataRobot Blog

JULY 27, 2022

In this technical post, we’ll focus on some changes we’ve made to allow custom models to operate as an algorithm on Algorithmia, while still feeding predictions, input, and other metrics back to the DataRobot MLOps platform —a true best of both worlds. Data Science Expertise Meets Scalability. Then select, Create new key.

Metrics 52

Metrics Deep Learning Modeling Machine Learning 52

Sisense

AUGUST 6, 2020

An obvious mechanical answer is: use relevance as a metric. Another important method is to benchmark existing metrics. Today, we’re developing AI in an era where data is treated as code, or at least as an extension of code, because the code alone cannot achieve deep learning without the data. The perfect fit.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

IBM Big Data Hub

DECEMBER 19, 2023

Anomaly detection simply means defining “normal” patterns and metrics—based on business functions and goals—and identifying data points that fall outside of an operation’s normal behavior. Supervised learning Supervised learning techniques use real-world input and output data to detect anomalies.

Machine Learning 67

Machine Learning Optimization Unstructured Data Sales 67

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

CIO Business Intelligence

APRIL 26, 2022

For personnel, cameras look for personal protective equipment (PPE) use, such as hard hats and safety glasses, and then the system either sends alerts to a manager if PPE is not being worn or keeps track of metrics that a safety officer uses to determine whether training is needed. Then, only the updated model is sent back to the edge.

Manufacturing 95

Manufacturing Optimization Metrics Deep Learning 95