Ontotext

SEPTEMBER 15, 2020

These additional software components need to be updated, tested and deployed, which goes counter to the Data Fabric goal of creating frictionless movement of data. Ontotext Platform ensures data is accessible to the people in the organization that need the data rather than depending on a technical staff to package it and ferry it to them.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Domino Data Lab

DECEMBER 4, 2020

Once all packages have been imported, we can move on to loading our test data. We can then proceed with pharmacokinetic modeling, testing the goodness of fit of various models. Note that the import may take a while due to the nature of the just-ahead-of-time (JAOT) compiler that Julia uses. Non Compartmental Analysis.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Ontotext

APRIL 4, 2019

Milena Yankova : Our work is focused on helping companies make sense of their own knowledge. Within a large enterprise, there is a huge amount of data accumulated over the years – many decisions have been made and different methods have been tested. Some of this knowledge is locked and the company cannot access it.

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

Domino Data Lab

JUNE 15, 2021

We can now test the function from our Domino Workspace (JupyterLab in this case): cur.execute("SELECT ADD(5,2)") cur.fetchone()[0]. Now let’s implement a simple machine learning scoring function against our test data. Running this DDL in Snowflake results in a “Function ADD successfully completed” message.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Ontotext

AUGUST 16, 2019

The training is structured to follow the steps of building a simple prototype to test the feasibility of the technology with hands-on guidance by experienced instructors. The answers to these questions are presented in the course of week-long, self-paced sessions and a 4.5-hour hour live online practice session.

Knowledge Discovery 40

Knowledge Discovery Technology Metadata Interactive 40

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

One way to check $f_theta$ is to gather test data and check whether the model fits the relationship between training and test data. This tests the model’s ability to distinguish what is common for each item between the two data sets (the underlying $theta$) and what is different (the draw from $f_theta$).

KDD 40

KDD Testing Machine Learning Measurement 40

The Unofficial Google Data Science Blog

JANUARY 14, 2016

For this purpose, let’s assume we use a t-test for difference between group means. Effect size thus defined is useful because the statistical power of a classical test for $delta$ being non-zero depends on $e/sqrt{tilde{n}}$, where $tilde{n}$ is the harmonic mean of sample sizes of the two groups being compared.

Experimentation 42

Experimentation Statistics Metrics Measurement 42

Domino Data Lab

AUGUST 1, 2021

After forming the X and y variables, we split the data into training and test sets. Next, we pick a sample that we want to get an explanation for, say the first sample from our test dataset (sample id 0). For sample 23 from the test set, the model is leaning towards a bad credit prediction. show_in_notebook(). Ribeiro, M.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

CIO Business Intelligence

MARCH 26, 2025

Search and knowledge discovery technology is required for organizations to uncover, analyze, and utilize key data. Now, a new wave of AI generative AI (GenAI) is changing how forward-looking organizations approach search, knowledge management, and other forms of knowledge discovery. How did we get here?

Knowledge Discovery 84

Knowledge Discovery Cost-Benefit Enterprise Modeling 84

Ontotext

AUGUST 21, 2024

As a result, contextualized information and graph technologies are gaining in popularity among analysts and businesses due to their ability to positively affect knowledge discovery and decision-making processes. This includes working with Subject Matter Experts to prioritize business objectives and build use case relationships.

Knowledge Discovery 52

Knowledge Discovery Cost-Benefit Data-driven Metadata 52