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 95

Enterprise Interactive Data-driven Key Performance Indicator 95

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

Insight

JANUARY 14, 2020

I was familiar with the strategy of using data augmentation techniques (such as flipping, shearing, zooming, etc.) Keras is an open source deep learning API that was written in Python and runs on top of Tensorflow, so it’s a little more user-friendly and high-level than Tensorflow. What the heck!

Metrics 58

Metrics Optimization Modeling Testing 58

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

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

Domino Data Lab

AUGUST 10, 2021

This change makes models the new currency of competitive advantage, strategy, and growth. PyTorch: used for deep learning models, like natural language processing and computer vision. It’s used for developing deep learning models. Deep Learning: designed to replicate the structure of the human brain.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. from sklearn import metrics. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set." Fraudulent transactions are 0.17% of the test set. 2f%% of the test set." Model training.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Smarten

APRIL 19, 2024

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

AWS Big Data

OCTOBER 26, 2023

We ran between 1–200 concurrent tests of this benchmark, simulating between 1–200 users trying to load a dashboard at the same time. To quantify this, we look at the price-performance using published on-demand pricing for each of the warehouses in the preceding test, shown in the following chart.

Data Warehouse 95

Data Warehouse Cost-Benefit Dashboards Optimization 95

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

Domino Data Lab

MARCH 3, 2019

What metrics are used to evaluate success? I’m here mostly to provide McLuhan quotes and test the patience of our copy editors with hella Californian colloquialisms. The data types used in deep learning are interesting. The data types used in deep learning are interesting. Who builds their models?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

DataRobot Blog

DECEMBER 20, 2022

We’ll then empirically test this assumption based on an example of real estate asset assessment. Automating and optimizing their investment strategy. By using this model, all accuracy metrics would also comply with national valuation regulations —as defined by the Bank of Spain.

Management 59

Management Machine Learning Optimization Modeling 59

O'Reilly on Data

JANUARY 25, 2021

Both SRE and DevOps emphasize similar practices: version control (62% growth for GitHub, and 48% for Git), testing (high usage, though no year-over-year growth), continuous deployment (down 20%), monitoring (up 9%), and observability (up 128%). It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 363

Machine Learning Software Testing Technology 363