O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Agreeing on metrics. The AI Product Development Process.

Marketing 361

Marketing Experimentation Metrics Testing 361

Jet Global

MAY 9, 2024

Data Migration Pipelines : These pipelines move data from one system to another, often for the purpose of upgrading systems or consolidating data sources. For example, migrating customer data from an on-premises database to a cloud-based CRM system. What is an ETL pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

CIO Business Intelligence

APRIL 7, 2023

So what are ways to store, structure, analyze, and disseminate data collected and centralized to develop digital solutions that promote this? This solution, she adds, allows developers to use data and run it through a machine learning algorithm with other users, approvals and third-party data to predict credit risk. “We

Management 92

Management Digital Transformation B2B Marketing 92

Occam's Razor

MARCH 30, 2017

Machine Learning | Marketing. Machine Learning | Analytics. People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Most Deep Learning methods involve artificial neural networks, modeling how our bran works.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Sisense

APRIL 10, 2020

NLP can be used on written text or speech data. For our example, we will use written text for our comparison of R vs Python for data science. We are surrounded by written text every day: emails, SMS messages, webpages, books, and much more. R vs Python for data science: Digging into the differences.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

CIO Business Intelligence

MARCH 29, 2022

It isn’t practical to save all your data, but it is important to realize data may be valuable for other projects. You lose that add-on value when you throw data away. . This type of data waste results in missing out on the second project advantage. That, in turn, makes it more likely to find the ones that pay off.

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Data Quality 52

Alation

FEBRUARY 13, 2020

Observers have called this “ The Second Machine Age ” and The Fourth Industrial Revolution.” More often than not, today, the key to unlocking that accomplishment sits within a tsunami of data: data collected from consumers, applications, and sensors. Each of your organizations has something impossible to accomplish.

Machine Learning 52

Machine Learning Data-driven Big Data Technology 52

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. often without even looking at it.

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

datapine

NOVEMBER 19, 2019

An important part of artificial intelligence comprises machine learning, and more specifically deep learning – that trend promises more powerful and fast machine learning. 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

Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Strong patterns, if found, will likely generalize to make accurate predictions on future data. Machine learning provides the technical basis for data mining.

Data mining 77

Data mining KDD Data Science Forecasting 77

IBM Big Data Hub

JUNE 29, 2023

enables manufacturers to conduct real-time data collection and analysis for vast amounts of data, providing them valuable insights into their operations. To find out how, book a live demo with an IBM expert The post 10 manufacturing trends that are changing the industry appeared first on IBM Blog. Industry 4.0

Manufacturing 55

Manufacturing Internet of Things Cost-Benefit Digital Transformation 55

Occam's Razor

OCTOBER 12, 2017

It is still on the books, and places extraordinary power in the President of the US to do what they want to people who might not look like "Americans." Here are the collection of books, videos, people and learning opportunities from my sweetspot…. I want to recommend three books. Intro to Machine Learning.

Marketing 136

Marketing Analytics Machine Learning Strategy 136

Sisense

MARCH 18, 2020

Where machines learn like humans and from humans, unconscious bias is as much a threat as with humans.” This danger looms greater than ever today thanks to machine learning techniques that are fundamentally changing the way software is made. outdated history books), supervising teams (e.g. Lothar Determann.

Software 52

Software Machine Learning Internet of Things Risk 52

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. Data collected from this system reflects the way the world works when we just observe it.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams. Friction ensued.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

O'Reilly on Data

JULY 20, 2021

The framework I’ve found most helpful is Contextual Integrity which was introduced by Helen Nissenbaum in the book Privacy in Context. We can’t forget that the machine learning that is doing biometrics is not a deterministic calculation; there is always some degree of uncertainty. Privacy is hard to understand.

Risk 303

Risk Software Interactive Modeling 303

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Missing values can be filled in based on expert knowledge, heuristics, or by some machine learning techniques.

Testing 68

Testing Modeling Interactive Measurement 68

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

O'Reilly on Data

OCTOBER 22, 2020

The safest course of action is also the slowest and most expensive: obtain your training data as part of a collection strategy that includes efforts to obtain the correct representative sample under an explicit license for use as training data. How I use it: I like to ask this as early as possible. How many people will use it?

Consulting 292

Consulting Risk Cost-Benefit Modeling 292

Domino Data Lab

OCTOBER 23, 2019

As Domino is committed to supporting data scientists and accelerating research, we reached out to Addison-Wesley Professional (AWP) Pearson for the appropriate permissions to excerpt “Predicting Social-Media Influence in the NBA” from the book, Pragmatic AI: An Introduction to Cloud-Based Machine Learning by Noah Gift.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Jet Global

NOVEMBER 15, 2021

The rise of artificial intelligence and robotic process automation gives a hint as to where machine learning capacity is headed towards. The use of specialized software can help your organization with the collection of data pertaining to KPIs and its reporting.

Metrics 52

Metrics Reporting KPI Risk Management 52

Jet Global

MAY 1, 2023

Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. While data exports may satisfy a portion of your customers, there will be many who simply want reports and insights that are available “out of the box.”

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52