Smart Data Collective

AUGUST 6, 2020

Thomas Griffin, the CEO of Optim Monster, points out that savvy marketers use artificial intelligence to enhance the quality of their video marketing strategies to expand their reach and multiply their conversion rates. You can use deep learning technology to replicate a voice that your audience will resonate with.

Deep Learning 81

Deep Learning Visualization Data-driven Marketing 81

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 79

Marketing Technology Advertising ROI 79

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

Smart Data Collective

JUNE 4, 2021

Probability is the measurement of the likelihood of events. The center of each cluster is the optimal location for setting up health centers. Deep Learning. Deep learning is a subset of machine learning that works similar to the biological brain. Use deep learning techniques for image recognition.

Data Science 104

Data Science Statistics Deep Learning Machine Learning 104

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). negation) detection.

Deep Learning 227

Deep Learning Testing Machine Learning Modeling 227

Cloudera

FEBRUARY 11, 2022

Advances in the development and application of Machine Learning (ML) and Deep Learning (DL) algorithms, require greater care to ensure that the ethics embedded in previous rule-based systems are not lost. Instead, they are learned by training a model on data. We then deploy the model and measure its ongoing performance.

Machine Learning 100

Machine Learning Sales Data Science Modeling 100

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 96

Enterprise Interactive Data-driven Key Performance Indicator 96

IBM Big Data Hub

NOVEMBER 29, 2023

Creating synthetic test data to expedite testing, optimization and validation of new applications and features. Here are two common metrics that, while not comprehensive, serve as a solid foundation: Leakage score : This score measures the fraction of rows in the synthetic dataset that are identical to the original dataset.

Metrics 81

Metrics Machine Learning Statistics Risk 81

Smart Data Collective

JULY 8, 2021

For instance, wireless sensor networks such as motes can make measurements of the building, monitor the temperature, etc. With a vast knowledge of previous projects, AI-assisted BIM can come up with the most optimal plan for future ones. And having the ability to learn from past successes and failures, it can improve by the second.

Cost-Benefit 125

Cost-Benefit Deep Learning Visualization Modeling 125

IBM Big Data Hub

AUGUST 11, 2023

MLOps is the next evolution of data analysis and deep learning. Simply put, MLOps uses machine learning to make machine learning more efficient. Conduct validation in the error process—This method measures how good the guesswork was by comparing it to known examples when available. MLOps and IBM Watsonx.ai

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

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.

Machine Learning 93

Machine Learning Testing Deep Learning Optimization 93

IBM Big Data Hub

JUNE 14, 2023

Traditional AI tools, especially deep learning-based ones, require huge amounts of effort to use. Datasets like this are measured in how many “tokens”—think of those as words or word parts—that we’re including. That sounds like a joke, but we’re quite serious.

Modeling 85

Modeling Data Lake Enterprise Deep Learning 85

Smart Data Collective

NOVEMBER 1, 2020

With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning? If you’ve ever come across deep learning, you might have heard about two methods to teach machines: supervised and unsupervised. Source ]. The Bottom Line.

Machine Learning 133

Machine Learning Data mining Visualization Statistics 133

CIO Business Intelligence

JANUARY 4, 2023

Workflow Optimization from Edge to Core/Cloud and Back : Integration with edge devices as well as integration with different HPC systems is currently designed in-house or otherwise customized. Of course, data center challenges are also driving demand for these alternatives. Just starting out with analytics?

Cost-Benefit 57

Cost-Benefit Machine Learning Modeling Uncertainty 57

O'Reilly on Data

JUNE 18, 2019

A recent O’Reilly survey found that those with mature AI practices (as measured by how long they’ve had models in production) cited “Lack of data or data quality issues” as the main bottleneck holding back further adoption of AI technologies. As model building become easier, the problem of high-quality data becomes more evident than ever.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

IBM Big Data Hub

APRIL 18, 2024

Beyond cost savings, organizations seek tangible ways to measure gen AI’s return on investment (ROI), focusing on factors like revenue generation, cost savings, efficiency gains and accuracy improvements, depending on the use case. AGI wouldn’t just perceive its surroundings; it would understand them.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

O'Reilly on Data

MARCH 31, 2020

Measurement, tracking, and logging is less of a priority in enterprise software. Many consumer internet companies invest heavily in analytics infrastructure, instrumenting their online product experience to measure and improve user retention. Managing Machine Learning Projects” (AWS). People + AI Guidebook” (Google).

Management 333

Management Machine Learning Experimentation Metrics 333

bridgei2i

OCTOBER 7, 2020

Sometimes due to excessive volume of data, an underwriter can get confused and is unable to measure risk appropriately. Importance of capturing market data for optimized pricing models. At the final stages of pricing optimization and quote generation, automation can recommend rates along with application & risk summary.

Insurance 52

Insurance Sales Strategy Digital Transformation 52

Cloudera

NOVEMBER 15, 2021

Deep Learning, a subset of AI algorithms, typically requires large amounts of human annotated data to be useful. Source Integrity and Measurement Accuracy : ensuring that prior human decisions and judgments (e.g., Here we briefly describe some of the challenges that data poses to AI. Data annotation. months since 2012.

Unstructured Data 93

Unstructured Data Recreation/Entertainment Structured Data Reporting 93

Cloudera

FEBRUARY 16, 2022

blueberry spacing) is a measure of the model’s interpretability. By logging the performance of every combination of search parameters within an experiment, we can choose the optimal set of parameters when building a model. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? Model Visibility.

Machine Learning 87

Machine Learning Modeling Metadata Recreation/Entertainment 87

BizAcuity

APRIL 6, 2022

To predict movements and volatility, machine learning and deep learning algorithms are widely used by organizations to strategize and prepare accordingly. Not using AI for predicting crypto price movements can be an extremely risky measure in the current financial climate.

Data Analytics 52

Data Analytics Analytics B2B Deep Learning 52

Jen Stirrup

MAY 5, 2020

You can also use Azure Data Lake storage as well, which is optimized for high-performance analytics. Databases also optimized very well for Apache Spark, and you can improve Apache Spark jobs by somewhere between 10 to 100 times. The Azure Data Lake Store is an optimized way of storing data, especially for analytics.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

Edwin Chen

OCTOBER 6, 2014

How do you measure its quality, to make sure that it's sending users relevant and personalized content? Metrics like CTR, or even number of favorites and retweets, will probably optimize for showing quick one-liners and pictures of funny cats. Is CTR a good measure? Take Google's search engine. What about revenue?

Metrics 79

Metrics Measurement Machine Learning Testing 79

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 also talked about the increased interest in green AI, in which we not only measure the quality of a model based on accuracy but also how big and complex it is.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 167

Analytics Machine Learning Data Science Deep Learning 167

AWS Big Data

OCTOBER 26, 2023

Read on to understand why price-performance matters and how Amazon Redshift price-performance is a measure of how much it costs to get a particular level of workload performance, namely performance ROI (return on investment). For low-cardinality data, there is another type of encoding that can be more optimal: BYTEDICT.

Data Warehouse 96

Data Warehouse Cost-Benefit Dashboards Optimization 96

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. This unified experience optimizes the process of developing and deploying ML models by streamlining workflows for increased efficiency.

Machine Learning 72

Machine Learning Manufacturing Cost-Benefit Deep Learning 72

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 of validation data sets and the accuracy measurements of stored models.

Modeling 193

Modeling Machine Learning Testing Metrics 193

IBM Big Data Hub

NOVEMBER 14, 2023

Internal data monetization initiatives measure improvement in process design, task guidance and optimization of data used in the organization’s product or service offerings. Realize : Each data service has an agreed unit of value which is transacted and measured.

Strategy 105

Strategy Cost-Benefit Data-driven Measurement 105

O'Reilly on Data

OCTOBER 19, 2021

Not only is data larger, but models—deep learning models in particular—are much larger than before. However, none of these layers help with modeling and optimization. We cannot expect data scientists to write modeling frameworks like PyTorch or optimizers like Adam from scratch! Model Operations.

IT 347

IT Testing Experimentation Software 347

DataKitchen

APRIL 13, 2021

Observe, optimize, and scale enterprise data pipelines. . DataOps requires that teams measure their analytic processes in order to see how they are improving over time. DataMo – Datmo tools help you seamlessly deploy and manage models in a scalable, reliable, and cost-optimized way. Monte Carlo Data — Data reliability delivered.

Testing 300

Testing Machine Learning Consulting Data Quality 300

CIO Business Intelligence

MARCH 22, 2022

For example, consider identifying a single, measurable use case for AI that gets you started with a proof of concept (PoC) that could be a stepping stone to other initiatives. 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.

Analytics 99

Analytics Key Performance Indicator Data Warehouse Data-driven 99

Smart Data Collective

MAY 17, 2021

A mission-critical task like maintenance can be relegated to proactive measures thanks to a steady flow of performance data. What’s more, the same technology can be used for other measures, like monitoring assets and goods, which cuts down on fraud and theft. Big data can help by building reliable driver and fleet profiles.

Big Data 100

Big Data Cost-Benefit IoT Machine Learning 100

Domino Data Lab

AUGUST 29, 2019

This article provides an excerpt “Deep Reinforcement Learning” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The article includes an overview of reinforcement learning theory with focus on the deep Q-learning. Essential Theory of Reinforcement Learning.

Optimization 111

Optimization Modeling Deep Learning Metrics 111

CIO Business Intelligence

MARCH 22, 2023

The new class often uses advanced techniques such as deep learning, natural language processing, and computer vision to analyze and extract insights from the data. Homomorphic encryption often requires custom hardware to optimize performance, which can be expensive to develop and maintain.

Machine Learning 102

Machine Learning Cost-Benefit Modeling Risk 102

Domino Data Lab

MAY 8, 2019

Visualizations are vital in data science work, with the caveat that the information that they convey may be 4-5 layers of abstraction away from the actual business process being measured. measure the subjects’ ability to trust the models’ results. Information can get quite distorted after being abstracted that many times.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Alation

APRIL 14, 2022

They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. Governance influences how an organization’s objectives are set and achieved, how risk is monitored and addressed, and how performance is optimized.” It’s not a simple definition.

Metadata 59

Metadata Data Governance Modeling Data-driven 59