O'Reilly on Data

JULY 28, 2020

In this article, we turn our attention to the process itself: how do you bring a product to market? Without clarity in metrics, it’s impossible to do meaningful experimentation. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources.

Marketing 361

Marketing Experimentation Metrics Testing 361

DataRobot Blog

JULY 28, 2022

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. Sensor Data Analysis Examples. The Best Way to Achieve Both Accuracy and Cost Control.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

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? Adapted from the book Effective Data Science Infrastructure. Data Science Layers. This approach is not novel.

IT 342

IT Testing Experimentation Software 342

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. Then, often reporting to risk, compliance, or security organizations, are separate data governance teams focused on data security, privacy, and quality.

Digital Transformation 145

Digital Transformation Data Governance Experimentation Machine Learning 145

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

DECEMBER 19, 2023

In researching this article, I found gaps where CIOs promised capabilities to stakeholders, but implementations and business impacts have lagged expectations. In the 2023 State of Data Science and Machine Learning Report , only 18% of respondents said that at least half their machine learning models make it into production.

Digital Transformation 142

Digital Transformation Unstructured Data Machine Learning Risk Management 142

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

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). XPU: One of the many specialized CPUs for specific applications (similar to an ASIC ), which may be real-time, data-intensive, data-specific, or at the Edge.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

CIO Business Intelligence

APRIL 15, 2024

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 The Tax Institute studies and analyzes the constantly shifting landscape of federal and state tax laws and publishes articles on how to deal with them.

Testing 83

Testing Experimentation Data Quality Machine Learning 83

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

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

Domino Data Lab

OCTOBER 14, 2020

This article focuses on accelerating model development. Experimentation and collaboration are built into the core of the platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Domino shines in reproducibility and discovery. Hyperparameter Tuning. Why Petastorm?

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

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. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Cloudera

FEBRUARY 16, 2022

In this article, we explore model governance, a function of ML Operations (MLOps). In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Model Visibility. Model Explainability.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. He also encouraged data scientists to understand how new data science algorithms rearrange power as well as how the history of data is a story of truth and power.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

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. Atlassian) MLOps Tooling (Todd Morrill) Building Machine Learning Powered Applications (O’Reilly) Designing Data-Intensive Applications (O’Reilly).

Management 240

Management Metrics Machine Learning Modeling 240

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 274

Deep Learning Machine Learning Data-driven Modeling 274

Insight

APRIL 28, 2020

Originally posted on Open Data Science (ODSC). 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. Designing a Data Science Interview Onsite interviews are indispensable, but they are time-consuming.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

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

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. Introduction. Welcome back to our monthly burst of themes and conferences.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Ontotext

NOVEMBER 2, 2023

This shift from relational to graph approach has been well-documented by Gartner who advise that “using graph techniques at scale will form the foundation of modern data and analytics” and “graph technologies will be used in 80% of data and analytics innovations by 2025.”

IT 69

IT Cost-Benefit Data-driven Technology 69

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

FineReport

DECEMBER 19, 2019

In addition, Jupyter Notebook is also an excellent interactive tool for data analysis and provides a convenient experimental platform for beginners. Data Analysis Libraries. In addition to the three types of tools mentioned above, there is actually a type of data analysis library that is more suitable for advanced data analysts.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

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

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

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. They have the foundations of data infrastructure. Yet, this challenge is not insurmountable.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

APRIL 7, 2019

If you are in research, excellent libraries like Allen NLP and NLP Architect are designed to make experimentation easier, although at the expense of feature completeness, speed and robustness. This article is intended to be a useful cheat sheet for narrowing down your choice. We assume that you need to build production-grade software.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

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 92

Cost-Benefit Modeling Manufacturing Interactive 92

Alation

SEPTEMBER 28, 2022

However, there is a lot more to know about DataOps, as it has its own definition, principles, benefits, and applications in real-life companies today – which we will cover in this article! This requires that data engineers embrace learning and integrating new technologies, such as AI tools. What Is DataOps? It’s a Team Sport.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

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

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

Modeling 59

Modeling Metadata Publishing Sales 59

Insight

MARCH 27, 2019

Discovering the proper method requires time-consuming experimentation. At the time of this article, the open source version of AutoAugment did not provide the controller module, which prevents users from running it for their own datasets. Want to advance your career in Data Science and Artificial Intelligence?

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

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. It can be ordered, discrete, or continuous.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

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

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

AUGUST 22, 2019

Data scientists and researchers require an extensive array of techniques, packages, and tools to accelerate core work flow tasks including prepping, processing, and analyzing data. Utilizing NLP helps researchers and data scientists complete core tasks faster.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111