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

Smart Data Collective

JULY 9, 2023

This article is going to provide some great insights on developing strategies for unlocking additional value from an online business, which can do a lot to boost revenue and catapult the enterprise to new heights. Experimentation is the key to finding the highest-yielding version of your website elements.

Optimization 74

Optimization Data Analytics Analytics Data-driven 74

CIO Business Intelligence

JUNE 11, 2024

A 1958 Harvard Business Review article coined the term information technology, focusing their definition on rapidly processing large amounts of information, using statistical and mathematical methods in decision-making, and simulating higher order thinking through applications.

IT 73

IT Digital Transformation Broadcasting Business Objectives 73

3AG Systems

DECEMBER 8, 2022

In 2022, Harvard Business Review posted an article by Thomas H. When the Data Scientist role “was relatively new” in 2012, the authors observed that “as more companies attempted to make sense of big data, they realized they needed people who could combine programming, analytics, and experimentation skills.”

Data Science 40

Data Science Experimentation Machine Learning Big Data 40

IBM Big Data Hub

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors.

Experimentation 74

Experimentation Modeling Deep Learning Data-driven 74

Smart Data Collective

OCTOBER 13, 2022

This article explores the possibilities and limits of AI in research and development. These patterns could then be used as the basis for additional experimentation by scientists or engineers. The Explainability Problem is the inability of machine learning systems to explain their decision-making processes.

Testing 113

Testing Cost-Benefit Experimentation Optimization 113

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

Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ).

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

CIO Business Intelligence

JUNE 28, 2023

Many CIOs have become the de facto generative AI professor and spent ample time developing 101 materials and conducting roadshows to build awareness, explain how generative AI differs from machine learning, and discuss the inherent risks. Experimentation with a use case driven approach. Building the right mindset is key.

Enterprise 52

Enterprise Experimentation Risk Data-driven 52

Decision Management Solutions

DECEMBER 17, 2021

In the recent McKinsey article discussing designing next-generation credit-decisioning models they outlined four best practices for automated credit-decisioning models for banks as they continue their digital transformations. Digital lending based on high-performance credit-decisioning models, says McKinsey, lead to: Increased revenue.

Modeling 52

Modeling Experimentation Visualization Risk 52

O'Reilly on Data

MAY 14, 2020

In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager. This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations.

Management 363

Management Experimentation B2B Machine Learning 363

Sisense

MARCH 26, 2021

To achieve this, he says, companies should find ways to lower the cost of experimentation, decrease the time to value, and scale successful experimentation into products quickly. Charles articulated this in a 2019 article in which he considered invisible analytics and embedded insights to be the future of business intelligence.

Analytics 98

Analytics Data-driven Dashboards Experimentation 98

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

Domino Data Lab

AUGUST 5, 2019

The article focuses on 1) proposed frameworks for defining and designing for ethics and for understanding the forces that encourage industry to operationalize ethics, as well as 2) proposed ethical principles for data scientists to consider when developing data-empowered products. data munging, building models, etc.).

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

Smart Data Collective

DECEMBER 24, 2020

Machine learning is the driving force of AI. It allows humans to essentially teach software in a matter of weeks what a human would take decades to learn. AI and machine learning are changing the world we live in and altering the way we do things. Some grad students have already learned this the hard way.

Machine Learning 96

Machine Learning Experimentation Software Technology 96

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. A complementary Domino project is available. . Introduction. Chapter Introduction: Causal Inference.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. It analyzes historical data and news articles, confirming a possible market correction.

Cost-Benefit 89

Cost-Benefit Modeling Manufacturing Interactive 89

Insight

APRIL 28, 2020

In this article, we share some data-driven advice on how to get started on the right foot with an effective and appropriate screening process. In the next post in this series, we’ll dive into the data and discuss the lessons learned on designing a successful onsite interview. Article by Adam Azzam. Length: Highly Variable.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

O'Reilly on Data

OCTOBER 13, 2020

Our previous articles in this series introduce our own take on AI product management , discuss the skills that AI product managers need , and detail how to bring an AI product to market. This is a fair question, but—as with all things related to machine learning and artificial intelligence—the answer is far from a binary yes or no.

Management 253

Management Metrics Machine Learning Modeling 253

Insight

MARCH 27, 2019

Discovering the proper method requires time-consuming experimentation. Even after many experiments, a machine learning (ML) engineer may still not discover the best option. AutoAugment was one of the most exciting publications in 2018, and the first method using reinforcement learning for this particular problem.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. However, the concept is quite abstract.

IT 351

IT Testing Experimentation Software 351

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

CIO Business Intelligence

MARCH 7, 2023

I’ll be covering more examples of force multipliers in upcoming articles, and here are three to start that should apply to most CIOs and their IT organizations. So what should CIOs look to do today to drive digital transformation, identify force multipliers, and define initiatives that enable smarter, safer, and faster business outcomes?

Digital Transformation 144

Digital Transformation Data Governance Experimentation Machine Learning 144

Rocket-Powered Data Science

JULY 6, 2021

1) Automated Narrative Text Generation tools became incredibly good in 2020, being able to create scary good “deep fake” articles. 2) MLOps became the expected norm in machine learning and data science projects. the monitoring of very important operational ML characteristics: data drift, concept drift, and model security).

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. At this point I’m resisting an urge to quote and analyze nearly all of that HBR article. hill climbing ) ( Clue #1 ).

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Insight

FEBRUARY 20, 2020

Only Bias Understanding why noise in machine learning is nothing but bias In this post, we explain how bias and noise in machine learning are two sides of the same coin. Read on to see how the same goes for the fundamental laws of machine learning. There is No Noise?—?Only God does not play dice.?—?Albert

Machine Learning 71

Machine Learning Experimentation Modeling Measurement 71

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. The field of statistical machine learning provides a solution to this problem, allowing exploration of larger spaces. Machine learning 45.1 Machine learning 20.3

Statistics 44

Statistics Optimization Modeling Experimentation 44

Insight

MARCH 12, 2020

In this article, I will discuss the construction of the AIgent, from data collection to model assembly. 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. In other words, if 0.1%

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. 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. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Data Science and Beyond

JULY 22, 2018

This article is a short summary of my understanding of the definition of data science in 2018. 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. In a recent article , Hernán et al. But what does it mean?

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. ” BTW, that Knuth article from 1983 was probably the first time that I ever saw the word “Web” used as a computer-related meaning. of relational databases represent early forms of machine learning.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Perceptual Edge

AUGUST 26, 2019

Pangilinan wrote chapter 9 of the book, titled “Data and Machine Learning Visual Design and Development in Spatial Computing,” which promotes VR’s usefulness for data visualization. Note: In case the term is unfamiliar, “spatial computing” apparently refers to VR, AR, and other related technologies). Her case is hollow.

Visualization 52

Visualization Cost-Benefit Interactive Experimentation 52

Cloudera

NOVEMBER 25, 2020

An integrated unstructured data match engine to find similar documents, reports, articles, and patents, using natural language and/or in combination with SQL. 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 59

Data Warehouse Unstructured Data Analytics Visualization 59

Occam's Razor

FEBRUARY 28, 2017

Throw in Machine Learning and I weep at how many glorious sales, marketing, deep relationships initiatives are impossible because companies have not solved identity. As you’ve read in the Forbes article, I love storytelling. Truly omg coolness. You, I’m talking about you!). See where you are, go up one step.

Key Performance Indicator 106

Key Performance Indicator Metrics Measurement Advertising 106

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.

Testing 79

Testing Modeling Machine Learning Metrics 79

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Tunnel vision may stem from inertia ("We only believe in live experiments"), tool preferences ("I am deeply in love with machine learning"), indolence ("Running a survey is a lot of work. only in enough detail to motivate the types of uncertainty that are central to this article. or just plain lack of imagination (" ").

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

CIO Business Intelligence

APRIL 15, 2024

Tax preparation company H&R Block is no stranger to AI and machine learning (ML), having leveraged the technologies across its business for years. Given the speed required, Lowden established a specialized team for the project to encourage a culture of experimentation and “moving fast to learn fast.” “You

Testing 80

Testing Experimentation Data Quality Machine Learning 80