Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. So, we have workspaces, projects and sessions in that order.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Insight

JANUARY 14, 2020

In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment. Choosing your loss function and optimizer Finally, in the last block of code, we must compile the model that we just built.

Metrics 58

Metrics Optimization Modeling Testing 58

IBM Big Data Hub

MAY 17, 2023

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. It can offer data on demand to different business units within an organization, with the help of various sensors and payloads. In a nutshell, DaaS provides simplified asset and value optimization.

Data Collection 80

Data Collection Visualization Data-driven Unstructured Data 80

IBM Big Data Hub

NOVEMBER 14, 2023

Data monetization is not narrowly “selling data sets ;” it is about improving work and enhancing business performance by better-using data. External monetization opportunities enable different types of data in different formats to be information assets that can be sold or have their value recorded when used.

Strategy 108

Strategy Cost-Benefit Data-driven Measurement 108

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. This blog post provides a concise session summary, a video, and a written transcript. We’ve got this complex landscape, tons of data sharing, an economy of data, external data, tons of mobile devices. Session Summary.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability. training data”) show the tangible outcomes. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

datapine

NOVEMBER 19, 2019

There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. An exemplary application of this trend would be Artificial Neural Networks (ANN) – the predictive analytics method of analyzing data.

Technology 279

Technology Internet of Things IT IoT 279

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.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Occam's Razor

OCTOBER 12, 2017

Like this blog, it will be particularly relevant for those who are in digital analytics and digital marketing. Optimal Starting SCOTUS Starting Points. If you would like to pursue my personal strategy, here are a collection of cases to use as starting points. Intro to Machine Learning. Machine Learning.

Marketing 136

Marketing Analytics Machine Learning Strategy 136