O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. If this sounds fanciful, it’s not hard to find AI systems that took inappropriate actions because they optimized a poorly thought-out metric.

Marketing 362

Marketing Experimentation Metrics Testing 362

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.” Product development : Generative AI is increasingly utilized by product designers for optimizing design concepts on a large scale.

Enterprise 122

Enterprise Modeling Testing Data-driven 122

Smarten

APRIL 19, 2024

Benefits include customized and optimized models, data, parameters and tuning. Price and bundling optimization, demand pricing, and other variables will be included to provide the best options, prices and responses and to personalize the approach and present results for data analysis and for end users.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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

AWS Big Data

OCTOBER 26, 2023

Some recent examples of performance optimizations driven by fleet telemetry include: String query optimizations – By analyzing how Amazon Redshift processed different data types in the Redshift fleet, we found that optimizing string-heavy queries would bring significant benefit to our customers’ workloads.

Data Warehouse 93

Data Warehouse Cost-Benefit Dashboards Optimization 93

CIO Business Intelligence

JUNE 1, 2022

These supercomputers power exciting innovations in deep learning, disease control, and physics—think bionic eyes, DNA sequencing for infectious disease research, and the study of time crystals. . CSIRO’s Bracewell Delivers Deep Learning, Bionic Vision. Bracewell’s IO500 score was 99.64, IO500 BW 39.90

Deep Learning 84

Deep Learning Snapshot Optimization Data Quality 84

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 96

Deep Learning Machine Learning Business Objectives Testing 96

Domino Data Lab

MARCH 16, 2021

Lately, however, there is very exciting research emerging around building concepts from first principles with the goal of optimizing the higher layers to be human-readable. Instead of optimizing for pure accuracy, the network is constructed in a way that focuses on strong definitions of high-level concepts. Saliency Maps.

Modeling 122

Modeling Deep Learning Testing Optimization 122

IBM Big Data Hub

SEPTEMBER 19, 2023

Auto-generated code suggestions can increase developers’ productivity and optimize their workflow by providing straightforward answers, handling routine coding tasks, reducing the need to context switch and conserving mental energy. Some generative AI for code tools automatically create unit tests to help with this.

Software 86

Software IT Visualization Deep Learning 86

CIO Business Intelligence

JUNE 13, 2022

The very best conversational AI systems come close to passing the Turing test , that is, they are very difficult to distinguish from a human being. . NLP technologies need to be thoughtfully trained and tested thoroughly to ensure they don’t have any biases. The answer depends on the scope of the application and throughput needs.

Data Processing 85

Data Processing Deep Learning Insurance Cost-Benefit 85

Domino Data Lab

MARCH 11, 2021

In this article, we’ll show key considerations for selecting the right machine learning framework for your project and briefly review four popular ML frameworks. Here are several key considerations you should take into account when selecting a machine learning framework for your project. Parameter Optimization. Tensorflow 2.0,

Machine Learning 143

Machine Learning Deep Learning Insurance Modeling 143

Decision Management Solutions

FEBRUARY 8, 2021

As you’ll see, the development of this amazing, one-of-a-kind vessel led to a conclusion that we at Decision Management Solutions see every day in our client work: It’s never enough to just rely on artificial intelligence (AI)/machine learning (ML) to do all the decision-making. Marine AI—based in Plymouth, U.K.—in

Machine Learning 93

Machine Learning Testing Deep Learning Optimization 93

IBM Big Data Hub

APRIL 18, 2024

Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. ” AGI analyzes relevant code, generates a draft function with comments explaining its logic and allows the programmer to review, optimize and integrate it.

Cost-Benefit 88

Cost-Benefit Modeling Manufacturing Interactive 88

CIO Business Intelligence

JUNE 1, 2022

The digital twins at McLaren are also used to run simulations for the design of new parts and then to test them for performance and reliability before they are manufactured and installed in the racing cars. How fast are product changes in Formula 1 racing design? McClaren releases product changes every 20 minutes. .

Manufacturing 97

Manufacturing Digital Transformation Predictive Analytics Strategy 97

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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.

Data Science 56

Data Science Machine Learning Cost-Benefit Deep Learning 56

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

For example, consider the following simple example fitting a two-dimensional function to predict if someone will pass the bar exam based just on their GPA (grades) and LSAT (a standardized test) using the public dataset (Wightman, 1998). Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Smart Data Collective

FEBRUARY 20, 2022

Its cost-effective service solutions ensure that you can optimize costs, organize data, and provide access controls to meet your business, organizational, and regulatory needs. AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms. Management of data. Messages and notification.

Cost-Benefit 114

Cost-Benefit Data Lake Software Machine Learning 114

CIO Business Intelligence

APRIL 25, 2022

Metacloud, giving AI developers the flexibility to run, test and deploy AI and ML workloads on mixed hardware within the same AI/ML workflow or pipeline. To create a productive, cost-effective analytics strategy that gets results, you need high performance hardware that’s optimized to work with the software you use.

Machine Learning 124

Machine Learning Enterprise Deep Learning Cost-Benefit 124

Domino Data Lab

OCTOBER 14, 2020

We needed an “evolvable architecture” which would work with the next deep learning framework or compute platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Hyperparameter Tuning. Putting it all together. The full example is available at [link].

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

IBM Big Data Hub

SEPTEMBER 6, 2023

Search engine optimization (SEO): Deploying an AI solution to enhance search engine optimization (SEO) helps marketers increase page rankings and develop more sound strategies. AI can help marketers create and optimize content to meet the new standards.

Marketing 83

Marketing Technology Advertising ROI 83

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. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

Deep Learning 59

Deep Learning Machine Learning Unstructured Data Modeling 59

IBM Big Data Hub

JULY 6, 2023

The evolution of machine learning The start of machine learning, and the name itself, came about in the 1950s. In 1950, data scientist Alan Turing proposed what we now call the Turing Test , which asked the question, “Can machines think?” Python is the most common programming language used in machine learning.

Machine Learning 57

Machine Learning Data Science Statistics Deep Learning 57

IBM Big Data Hub

NOVEMBER 29, 2023

Creating synthetic test data to expedite testing, optimization and validation of new applications and features. They are already identifying and exploring several real-life use cases for synthetic data, such as: Generating synthetic tabular data to increase sample size and edge cases.

Metrics 86

Metrics Machine Learning Risk Statistics 86

bridgei2i

MARCH 9, 2021

Machine Learning: Since intelligence without learning isn’t intelligence, this subset of AI focuses on parsing data and modifying itself without human effort. Deep Learning: DL falls under ML, but its capabilities aren’t comparable. ML techniques provide better data-based outputs over time.

Technology 52

Technology Deep Learning Data-driven Optimization 52

CIO Business Intelligence

JUNE 2, 2022

For optimizing existing resources, Eni uses HPC5 to model, study, and ultimately improve refinement operations. . Specifically, they are interested in electric utility response to cyber and physical threats, and they are working to develop an algorithm that can be used as a tested, trusted safeguard. HPCG [TFlop/s].

Informatics 84

Informatics Modeling Testing Technology 84

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. It signifies a shift in human-digital interaction, offering enterprises innovative ways to engage with their audience, optimize operations, and further personalize their customer experience. billion by 2030.

Enterprise 103

Enterprise Interactive Data-driven Key Performance Indicator 103

CIO Business Intelligence

APRIL 24, 2023

How we got here The most notable enabling technologies in generative AI are deep learning, embeddings, transfer learning (all of which emerged in the early to mid-2000s), and neural net transformers (invented in 2017). Deep learning emerged in academia in the early 2000s, with broader industry adoption starting around 2010.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

DataRobot

OCTOBER 20, 2021

rule-based AI , machine learning , deep learning , etc.) Evaluation metrics for machine learning models: Understanding evaluation metrics, what they optimize for, and how they intersect with AI fairness principles gives stakeholders the language necessary to qualify risks associated with AI systems. WHITE PAPER.

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

Domino Data Lab

JULY 28, 2021

It is the job of a data scientist to navigate these subtle differences, pick the model that aligns best with the problem statement, optimize and monitor performance and translate the findings back into a business context. After thorough review, revision, possible unit-testing, code reviews and the greenlight from any relevant stakeholders.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

FEBRUARY 14, 2024

Process automation is often centered on efforts to optimize spend, achieve greater operational efficiency and incorporate new and innovative technologies, which often translate into a better customer experience. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Cost-Benefit 86

Cost-Benefit Insurance Unstructured Data Machine Learning 86

Domino Data Lab

JULY 2, 2019

SQL optimization provides helpful analogies, given how SQL queries get translated into query graphs internally , then the real smarts of a SQL engine work over that graph. Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Software writes Software?

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Insight

APRIL 9, 2019

For industries outside of tech, ML can be utilized to personalize a user’s experience, automate laborious tasks and optimize subjective decision making. The biggest categories of cost for hardware designers and manufacturers are testing, verification, and calibration of their control systems. It is far from an automated process.

Manufacturing 75

Manufacturing Machine Learning Testing Software 75

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.

Data Architecture 69

Data Architecture Deep Learning Machine Learning Big Data 69

Domino Data Lab

DECEMBER 1, 2019

Given the proliferation of interest in deep learning in the enterprise, models that ingest non traditional forms of data such as unstructured text and images into production are on the rise. Step 4: Generate the test, train and noisy MNIST data sets. Detecting image drift. x_test = x_test.astype('float32') / 255.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

Domino Data Lab

JANUARY 14, 2019

Let’s build the models that we’ll use to test SHAP and LIME. xgb_model = xgb.train({'objective':'reg:linear'}, xgb.DMatrix(X_train, label=y_train)) # GBT from scikit-learn? To keep it simple, I choose to explain the first record in the test set for each model using SHAP and LIME. # X,y = shap.datasets.boston()?X_train,X_test,y_train,y_test

Testing 89

Testing Modeling Deep Learning Data Science 89

CIO Business Intelligence

APRIL 4, 2022

Find out more and take a test drive in one of Dell Technologies’ worldwide Customer Solution Centers. To create a productive, cost-effective analytics strategy that gets results, you need high performance hardware that’s optimized to work with the software you use. The world is only getting more complex.

Technology 52

Technology Deep Learning Cost-Benefit Data Quality 52