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. Moreover, M-LLMs adeptly answer questions about visual content, aiding in tasks like image recognition and scene understanding.

Visualization 132

Visualization Modeling Experimentation Testing 132

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

Domino Data Lab

MARCH 16, 2021

We are at an interesting time in our industry when it comes to validating models – a crossroads of sorts when you think about it. There is an opportunity for practitioners and leaders to make a real difference by championing proper model validation. Explaining how deep neural networks work is hard to do. Saliency Maps.

Modeling 122

Modeling Deep Learning Testing Optimization 122

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

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

CIO Business Intelligence

JANUARY 19, 2024

The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. Candidates for the exam are tested on ML, AI solutions, NLP, computer vision, and predictive analytics.

Data Science 126

Data Science Machine Learning Predictive Modeling Experimentation 126

Corinium

JUNE 6, 2019

Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. There is usually a steep learning curve in terms of “doing AI right”, which is invaluable. What advances do you see in Visual Analytics in the next five years?

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

CIO Business Intelligence

JUNE 7, 2022

To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

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 89

Software IT Visualization Deep Learning 89

O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). IBM Watson NLU.

Deep Learning 227

Deep Learning Testing Machine Learning Modeling 227

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. It culminates with a capstone project that requires creating a machine learning model.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

Smart Data Collective

SEPTEMBER 30, 2021

Throughout the past, it took years and sometimes even decades to develop a new vaccine, However, just a few moments after the pandemic has taken over our everyday lives, vaccine candidates were undergoing human tests. Other advanced technologies like deep learning algorithms are also vital for the development of quantum computing research.

Deep Learning 97

Deep Learning Machine Learning Business Objectives Testing 97

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

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 60

Machine Learning Data Science Statistics Deep Learning 60

Domino Data Lab

MARCH 11, 2021

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry, notably finance , insurance , healthcare , and marketing. TensorFlow runs on both CPUs and GPUs. Tensorflow 2.0,

Machine Learning 143

Machine Learning Deep Learning Insurance Modeling 143

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. IDEs (also sometimes referred to as notebooks) is a coding tool that makes it easier to develop and test code.

Data Science 145

Data Science Visualization Modeling Enterprise 145

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 59

Data Science Machine Learning Cost-Benefit Deep Learning 59

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 97

Cost-Benefit Modeling Manufacturing Interactive 97

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 136

Machine Learning Data Science Data Lake Modeling 136

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

CIO Business Intelligence

MARCH 18, 2024

By the time information slogged through every layer of our hierarchical operating model, all comparative advantages were lost. The enemy’s model, however, amplified the power of their bargain-bin tech. I believe large language models (LLMs) will prompt most organizations to face the same reckoning. Our solution?

Broadcasting 72

Broadcasting Deep Learning Risk Strategy 72

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

Domino Data Lab

OCTOBER 14, 2020

We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. For example, our models can show farmers how to increase their production while using less fertilizer.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

CIO Business Intelligence

SEPTEMBER 6, 2023

The offer ranges from AI and machine learning for beginners, to the Prompt Engineering learning path, to training courses on programming languages ​​such as Python, deep learning and neural networks, reinforcement learning, RPA, and natural language processing. We’re currently testing this using ChatGPT.

Digital Transformation 111

Digital Transformation Interactive Machine Learning Deep Learning 111

Insight

OCTOBER 25, 2019

This category was not considered for the purpose of this project as it does not allow for a 3-way partition for disjoint training, validation, and testing sets. My client also specified that CAD model files of the T-LESS dataset be used for this project, and that one object per class be reserved for testing (Objects 4, 8, 12, 18, 23, 30).

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

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 89

Cost-Benefit Insurance Unstructured Data Machine Learning 89

Cloudera

FEBRUARY 1, 2022

At Cloudera, we provide our customers with an extensive library of prebuilt data science projects (complete with out of the box models and apps) called Applied ML Prototypes (AMPs) to help them move the starting point of their project closer to the finish line. A perfect playground for computer vision models. Launch the AMP.

Deep Learning 108

Deep Learning Machine Learning Modeling Data Science 108

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. Data literacy is the ability to understand data science and AI applications critically using basic data visualization, communication, and reasoning skills. What Is Data Literacy? WHITE PAPER. Download Now.

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

Grooper

AUGUST 19, 2019

An interview with Pranshuk Kathed, machine and deep learning enthusiast. For simple reporting projects, I might spend 8 hours getting the right data and then just a couple of hours producing the needed visualizations. First, I have to understand the business model to actually see. What is machine learning?

Machine Learning 40

Machine Learning Metrics Descriptive Analytics Testing 40

Domino Data Lab

JUNE 23, 2019

Be aware that machine learning often involves working on something that isn’t guaranteed to work. As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. It is similar to R&D. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MARCH 3, 2019

The first survey started as a simple exploration into mainstream adoption of machine learning (ML). What’s been the impact of using ML models on culture and organization? Who builds their models? We also used maturity , in other words how long had an enterprise organization been deploying ML models in production?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JULY 9, 2019

Assessing whether a business stakeholder is trying to solve for a problem that is descriptive, predictive, or prescriptive and then re-framing the problem as supervised learning, unsupervised learning, or reinforcement learning, respectively. One of the ways I frame that is, “Are you looking to build a predictive model?

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Insight

JANUARY 14, 2020

CNNs have been widely considered state-of-the-art tools for computer vision since 2012, when AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). a “pre-trained” model) to be recycled and reused for many different tasks. You can use this metric to track progress during training or choose between different models.

Metrics 58

Metrics Optimization Modeling Testing 58

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

DataRobot

NOVEMBER 9, 2021

Signal classification models are typically built using time series principles; traditionally used features that include central, windowed, lag, and lead statistics can do the job but sometimes there might be scenarios where we want to eke out more performance out of the data. Image courtesy towardsAI.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52