Smart Data Collective

DECEMBER 21, 2020

Insurers are already using AI to select rates for customers and measure the risk they may pose, but how will it directly be of use in claims processing? At present, insurers use AI to assess individuals’ risk using quite generalized metrics, often based on their age, location, and gender. Are we close to AI reliance?

Insurance 134

Insurance Cost-Benefit Big Data Unstructured Data 134

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 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

IBM Big Data Hub

SEPTEMBER 6, 2023

More accurate measurement of KPIs: Digital campaigns generate more data than humans can keep up with, which can make measuring the success of marketing initiatives difficult. Step 4: Test the quality of data The success of an AI marketing tool depends on the accuracy and relevancy of the data it’s trained on.

Marketing 84

Marketing Technology Advertising ROI 84

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. The technology’s ability to adapt and learn from interactions further refines customer support metrics, including response time, accuracy of information provided, customer satisfaction and problem-resolution efficiency.

Enterprise 104

Enterprise Interactive Data-driven Key Performance Indicator 104

Domino Data Lab

JULY 17, 2021

Further, deep learning methods are built on the foundation of signal processing. The likelihood function measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters. VoxCeleb1 dev test # of speakers 1,211 40 # of videos 21,819 677 # of utterances 148,642 4,874.

Modeling 64

Modeling Testing Machine Learning Measurement 64

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

Edwin Chen

OCTOBER 6, 2014

How do you measure its quality, to make sure that it's sending users relevant and personalized content? Click-through rate may be your initial hope…but after a bit of thought, it's not clear that it's the best metric after all. Is CTR a good measure? So why, so often, do we never try to measure the relevance of our models?

Metrics 79

Metrics Measurement Machine Learning Testing 79

Cloudera

FEBRUARY 16, 2022

blueberry spacing) is a measure of the model’s interpretability. They define each stage from data ingest, feature engineering, model building, testing, deployment and validation. Figure 04: Applied Machine Learning Prototypes (AMPs). Machine Learning Model Reproducibility . Model Visibility.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

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 194

Modeling Machine Learning Testing Metrics 194

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Testing and Data Observability. Production Monitoring and Development Testing.

Testing 300

Testing Machine Learning Consulting Data Quality 300

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

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). Amazon Redshift delivers up to 4.9 times lower cost per user and up to 7.9

Data Warehouse 98

Data Warehouse Cost-Benefit Dashboards Optimization 98

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. It is similar to R&D. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Sisense

AUGUST 6, 2020

This is also an important takeaway for teams seeking to implement AI successfully: Start with the key performance indicators (KPIs) you want to measure your AI app’s success with, and see where that dovetails with your expert domain knowledge. An obvious mechanical answer is: use relevance as a metric. The perfect fit.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Insight

MARCH 25, 2020

After reading this, I hope you can learn how to build deep learning models using TensorFlow Keras, productionalize the model as a Streamlit app, and deploy it as a Docker container on Google Cloud Platform (GCP) using Google Kubernetes Engines (GKE). In this project, I was curious to see if deep learning approaches?—?specifically

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53

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

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. See Wei et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139