O'Reilly on Data

JUNE 19, 2019

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. A catalog or a database that lists models, including when they were tested, trained, and deployed.

Modeling 254

Modeling Machine Learning Testing Metrics 254

O'Reilly on Data

JUNE 7, 2022

We have a lot of vague notions about the Turing test, but in the final analysis, Turing wasn’t offering a definition of machine intelligence; he was probing the question of what human intelligence means. And granted, a lot can be done to optimize training (and DeepMind has done a lot of work on models that require less energy).

Modeling 364

Modeling Interactive Optimization Deep Learning 364

Cloudera

DECEMBER 4, 2024

The service is targeted at the production-serving end of the MLOPs/LLMOPs pipeline, as shown in the following diagram: It complements Cloudera AI Workbench (previously known as Cloudera Machine Learning Workspace), a deployment environment that is more focused on the exploration, development, and testing phases of the MLOPs workflow.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

DataRobot

JULY 27, 2021

Algorithmia automates machine learning deployment, provides maximum tooling flexibility, optimizes collaboration between operations and development, and leverages existing software development lifecycle (SDLC) and continuous integration/continuous development (CI/CD) practices. Request a Demo.

Optimization 98

Optimization Modeling Machine Learning Deep Learning 98

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 56

Deep Learning Machine Learning Unstructured Data Modeling 56

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 125

Deep Learning Machine Learning Business Objectives Testing 125

Insight

SEPTEMBER 20, 2019

Segmentation Since a few patients had multiple images in the dataset, the data were separated, by patient, into three parts: training (80%), validation (10%), and testing (10%). The box plot below shows a summary of the testing results. This shows that the model indeed learned where and what to look for in the images.

Deep Learning 53

Deep Learning Cost-Benefit Testing Modeling 53

DataRobot

FEBRUARY 10, 2021

Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger is a hands-on guide that helps people with little math background understand and use deep learning quickly. I tested this dataset because it appears in various benchmarks by Google and fast.ai.

Modeling 98

Modeling Deep Learning Machine Learning Metrics 98

CIO Business Intelligence

DECEMBER 15, 2023

Smart maintenance: Connected sensors can help city crews optimize maintenance activities such as garbage pickup, street cleaning, and snow removal to reduce costs and traffic impacts. In fact, new performance testing shows they’re powerful enough for smart city use cases even when running on CPU only. Artificial Intelligence

Testing 96

Testing Deep Learning Optimization Data Collection 96

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

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

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 105

Deep Learning Snapshot Optimization Machine Learning 105

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 Forecasting 126

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 138

Cost-Benefit Data Lake Software Machine Learning 138

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

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 111

Machine Learning Deep Learning Modeling Interactive 111

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 116

Data Warehouse Cost-Benefit Dashboards Optimization 116

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 119

Manufacturing Digital Transformation Predictive Analytics Strategy 119

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.

Deep Learning 105

Deep Learning Data Processing Insurance Cost-Benefit 105

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 134

Machine Learning Enterprise Deep Learning Cost-Benefit 134

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. testing every possible combination) Hyperparameter tuning is beneficial to some extent, but the real efficiency gains are in finding the right data.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

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

Smarten

JUNE 14, 2023

While it has only recently received public attention, this deep learning model architecture was launched more than five years ago by The American Artificial Intelligence Organization (OpenAI), and recent innovations like ChatGPT are based on the latest version of GPT (GPT-4).

Optimization 52

Optimization IT Digital Transformation Consulting 52

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 118

Enterprise Testing Modeling Data-driven 118

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 Data Science Machine Learning Data-driven 105

O'Reilly on Data

MARCH 31, 2020

This has serious implications for software testing, versioning, deployment, and other core development processes. At measurement-obsessed companies, every part of their product experience is quantified and adjusted to optimize user experience. Managing Machine Learning Projects” (AWS). People + AI Guidebook” (Google).

Management 363

Management Machine Learning Experimentation Metrics 363

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 Predictive Analytics Recreation/Entertainment 110

Cloudera

FEBRUARY 1, 2022

In this blog we’ll dig into how the Deep Learning for Image Analysis AMP can be reused to find snowflakes that are less similar to one another. If you are a Cloudera customer and have access to CML or Cloudera Data Science Workbench (CDSW), you can start out by deploying the Deep Learning for Image Analysis AMP from the “AMPs” tab. .

Deep Learning 111

Deep Learning Machine Learning Modeling Data Science 111

CIO Business Intelligence

AUGUST 21, 2024

The flashpoint moment is that rather than being based on rules, statistics, and thresholds, now these systems are being imbued with the power of deep learning and deep reinforcement learning brought about by neural networks,” Mattmann says. Plus, each agent can be optimized for its specific tasks.

Risk 136

Risk Insurance Cost-Benefit Software 136

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 98

Informatics Modeling Deep Learning Testing 98

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 Deep Learning Modeling Data Science 89

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

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 80

Software IT Visualization Deep Learning 80

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.

Machine Learning 77

Machine Learning Modeling Deep Learning Testing 77

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 78

Marketing Technology Advertising Machine Learning 78

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. Agile was originally about iterating fast on a code base and its unit tests, then getting results in front of stakeholders. Generally, you cannot get both.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105