Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). A reference to a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, Machine Learning, and real-time data. Examples: Cars, Trucks, Taxis. See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

O'Reilly on Data

MARCH 19, 2020

Aggregating artificial intelligence and machine learning topics accounts for nearly 5% of all usage activity on the platform, a touch less than, and growing 50% faster than, the well-established “data science” topic (see Figure 2). The chatbot was one of the first applications of AI in experimental and production usage.

Deep Learning 276

Deep Learning Machine Learning Data-driven Modeling 276

Data Science and Beyond

DECEMBER 23, 2018

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. The book focuses on randomised controlled trials and well-defined interventions as the basis of causal inference from both experimental and observational data.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Insight

MARCH 12, 2020

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. The AIgent was built with BERT, Google’s state-of-the-art language model. More relevant to the AIgent is Google’s BERT model, a task-agnostic (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. Data Scientists, Tradeoffs, and Data Ethics. As more companies become model-driven , data scientists are uniquely positioned to drive innovation and to help their companies remain economically strong.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 98

Cost-Benefit Modeling Manufacturing Interactive 98

Domino Data Lab

APRIL 7, 2019

A good NLP library will make it easy to both train your own NLP models and integrate with the downstream ML or DL pipeline. A good NLP library should also implement the latest and greatest algorithms and models – not easy while NLP is having its ImageNet moment and state-of-the-art models are being outpaced twice a month.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

Ontotext

NOVEMBER 2, 2023

The pathway forward doesn’t require ripping everything out but building a semantic “graph” layer across data to connect the dots and restore context. However, it will take effort to formalize a shared semantic model that can be mapped to data assets, and turn unstructured data into a format that can be mined for insight.

IT 69

IT Cost-Benefit Data-driven Technology 69

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. Data Analysis Libraries.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

FineReport

DECEMBER 11, 2019

In the past few years, the term “data science” has been widely used, and people seem to see it in every field. Big Data”, “Business Intelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis. By Michael Milton.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

The Unofficial Google Data Science Blog

JANUARY 16, 2018

by SANGHO YOON In this article, we discuss an approach to the design of experiments in a network. We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. However, the downside of using a larger unit of randomization is that we lose experimental power.

Testing 58

Testing Experimentation Measurement Modeling 58

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. In this article, we shift our focus to the AI Product Manager’s skill set, as it is applied to day to day work in the design, development, and maintenance of AI products.

Management 363

Management Experimentation B2B Machine Learning 363

Business Over Broadway

MAY 22, 2019

Below is a list of topics, answers and articles in support of a recent Tweet Chat in which I was the guest. I have included the topics of discussion, some of my answers and a list of articles in support of the week’s topic. So, become data literate. The chat (#CXChat) was on customer experience and emerging technologies.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

Insight

MARCH 27, 2019

It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. Introduction Data is the most critical piece of AI applications. Not having enough labeled data often leads to overfitting, which means the model will not be able to generalize to unseen examples. 2018 , blog ).

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

O'Reilly on Data

OCTOBER 19, 2021

In this article, we want to dig deeper into the fundamentals of machine learning as an engineering discipline and outline answers to key questions: Why does ML need special treatment in the first place? Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 346

IT Testing Experimentation Software 346

Data Science and Beyond

JULY 22, 2018

I got my first data science job in 2012, the year Harvard Business Review announced data scientist to be the sexiest job of the 21st century. Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. But what does it mean?

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

The Unofficial Google Data Science Blog

JULY 22, 2020

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. Companies like Google [2], Amazon [3], and Microsoft [4] have all published scholarly articles on this topic.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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 can use a regression model. A complementary Domino project is available. . Introduction.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

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. The field of AI product management continues to gain momentum.

Management 244

Management Metrics Machine Learning Modeling 244

Rocket-Powered Data Science

JULY 6, 2021

I provide below my perspective on what was interesting, innovative, and influential in my watch list of the Top 10 data innovation trends during 2020. 1) Automated Narrative Text Generation tools became incredibly good in 2020, being able to create scary good “deep fake” articles.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

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. CIOs should consider technologies that promote their hybrid working models to replace in-person meetings.

Digital Transformation 145

Digital Transformation Data Governance Experimentation Machine Learning 145

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 44

Statistics Optimization Modeling Experimentation 44

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

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. In other words, using metadata about data science work to generate code. Introduction. That’s impressive.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. This blog post introduces the notions of representational uncertainty and interventional uncertainty to paint a fuller picture of what the practicing data scientist is up against. Vignette: Data Science at fluff.ai

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

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. In [1]: # setup.

Testing 79

Testing Modeling Machine Learning Metrics 79

CIO Business Intelligence

APRIL 15, 2024

We’ve had what I’d consider more traditional AI and machine learning for over a decade, with thousands of models we’ve built and applied, mostly in service to tax by analyzing the millions of tax returns we have,” says H&R Block CIO Alan Lowden. It was a pretty short timeframe from ideation to delivery.” We don’t write all the code.

Testing 102

Testing Experimentation Data Quality Machine Learning 102

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Domino Data Lab

OCTOBER 14, 2020

We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. For example, our models can show farmers how to increase their production while using less fertilizer.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99