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. Without clarity in metrics, it’s impossible to do meaningful experimentation. Experimentation should show you how your customers use your site, and whether a recommendation engine would help the business.

Marketing 362

Marketing Experimentation Metrics Testing 362

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning. (4) Industry 4.0

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. And we can keep repeating this approach, relying on intuition and luck. Why experiment with several parameters concurrently?

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

DataRobot Blog

JULY 28, 2022

Rapid advances in machine learning in recent years have begun to lower the technical hurdles to implementing AI, and various companies have begun to actively use machine learning. The accuracy of machine learning models is highly dependent on the quality of the training data. Sensor Data Analysis Examples.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Also, loyalty leaders infuse analytics into CX programs, including machine learning, data science and data integration.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

CIO Business Intelligence

MARCH 21, 2022

Data scientists are becoming increasingly important in business, as organizations rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies. Data scientist job description.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Structured Data 137

DataRobot

JANUARY 13, 2022

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. It offers a visual and intuitive UI that enables anyone to explore and prepare data for machine learning, no matter their previous machine-learning experience.

ROI 52

ROI Machine Learning Experimentation Data Science 52

Occam's Razor

MARCH 23, 2021

A key part of how this manifested in our work was doing truly super-advanced machine-learning powered analysis to answer hard questions that few can successfully. We’ve chosen to use machine learning algorithms that learn from the underlying structures inside massive amounts of our datasets without explicit programming.

Analytics 131

Analytics Scorecard Strategy Testing 131

DataRobot

JUNE 8, 2021

Although modern medicine is founded on rigorous experimental design and statistical analysis, and many research studies have shown the superiority of objective analysis over human intuition, medical AI adoption will depend on consumer receptivity and trust in this new technology.

Data-driven 98

Data-driven Testing Marketing Experimentation 98

Domino Data Lab

JANUARY 24, 2019

These “automated machine learning” solutions help spread data science work by getting non-expert data scientists in to the model building process, offering drag-and-drop interfaces. This group of solutions targets code-first data scientists who use statistical programming languages and spend their days in computational notebooks (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Among these, only statistical uncertainty has formal recognition.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

FineReport

DECEMBER 19, 2019

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Ontotext

APRIL 3, 2024

W hen you try to automate business processes, semantics, and machine learning , knowledge graphs can bring a lot of value. Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. These are not statistical inferences. What is a customer?

Technology 59

Technology Risk Digital Transformation Measurement 59

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. You saw in the previous chapter that conditioning can break statistical dependence. Introduction.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. While AGI promises machine autonomy far beyond gen AI, even the most advanced systems still require human expertise to function effectively.

Cost-Benefit 96

Cost-Benefit Modeling Manufacturing Interactive 96

Insight

APRIL 28, 2020

You’ll often see the name “data challenge” used when the take-home assignment involves machine learning or statistics or “coding challenge” when the focus is on evaluating a candidate’s software engineering skills. Length: Highly Variable.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Domino Data Lab

DECEMBER 4, 2020

Domino Lab supports both interactive and batch experimentation with all popular IDEs and notebooks (Jupyter, RStudio, SAS, Zeppelin, etc.). We can group by study arm and calculate various statistics as mean and standard deviation. In this tutorial we will use JupyterLab. We can extract the two in a separate DataFrame.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Paul DeBeasi

JANUARY 30, 2019

Machine learning projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains, including statistical analysis, data analysis and application development. Four Options for Integrating Machine Learning with IoT.

Machine Learning 56

Machine Learning IoT Experimentation Statistics 56

Occam's Razor

JUNE 4, 2018

I mean developing and inserting a subtle collection of gentle nudges that can help increase the conversion rate by a statistically significant amount. And, if you have a GDPR compliant login mechanism…Does your machine learning-powered ecommerce platform leverage the lifetime of my site experience, complaints, purchases, etc.,

Strategy 124

Strategy Cost-Benefit Testing Sales 124

Business Over Broadway

FEBRUARY 21, 2021

As such, data science requires three broad skill sets , including subject matter expertise, statistics/math and technology/programming. Kaggle conducted a worldwide survey of over 20000 data professionals to learn about the state of data science and machine learning. What broad activities make up their respective jobs?

Machine Learning 60

Machine Learning Statistics Data Science Experimentation 60

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

JULY 24, 2018

The matrix also includes what is likely the world’s first widely available machine learning-powered metric: Session Quality , which you’ll find roughly in the middle. Ignore the metrics produced as an experimental exercise nine months ago. Ignore the metrics whose only purpose is to float along the river of data pukes.

Analytics 93

Analytics Metrics Strategy Measurement 93

Insight

MARCH 12, 2020

The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. After some experimentation, I landed on a strategy I’ll call ‘warm encoding’: if greater than 1% of tags were in a particular class, I encoded the book as belonging to that class, non-exclusively.

Modeling 59

Modeling Metadata Publishing Sales 59

Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. It’s not all about machine learning. It is now much easier to deploy machine learning models, even without a deep understanding of how they work.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Domino Data Lab

MAY 8, 2019

I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Corinium

APRIL 25, 2019

For example auto insurance companies offering to capture real-time driving statistics from policy-holders’ cars to encourage and reward safe driving. Machine learning can keep up, by continually looking for trends and anomalies, or predictive analytics, that are interesting for the given use case.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Cloudera

NOVEMBER 25, 2020

How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data? A seamless way to apply Machine Learning (ML) to the same data sets, without switching systems and copying data into and out of additional, possibly proprietary formats.

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. Note also that this account does not involve ambiguity due to statistical uncertainty. We sliced and diced the experimental data in many many ways.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Domino Data Lab

AUGUST 26, 2019

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Introduction. Cross-Validatory.

Testing 79

Testing Modeling Machine Learning Metrics 79

CIO Business Intelligence

JULY 6, 2023

After completing MTech from Indian Statistical Institute, I started my career at Cognizant. One of the major changes is the shift from signature-based protection to behavior-based Machine Learning dependent solutions. What do you do to foster a culture of innovation and experimentation in your employees?

Digital Transformation 98

Digital Transformation Experimentation Recreation/Entertainment Technology 98

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] Note: In more technical machine learning terms, the cost function of the skip-gram architecture is to maximize the log probability of any possible context word from a corpus given the current target word.] Learning phrase. Example 11.6

Deep Learning 111

Deep Learning Modeling Metrics Testing 111