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. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Business Over Broadway

AUGUST 4, 2020

Experimentation and iteration to improve existing ML models (39%). These top work activities included influencing business decisions, building prototypes to expand machine learning to new areas and improving ML models. Build prototypes to explore applying machine learning to new areas (52%).

Machine Learning 145

Machine Learning Data Science Statistics Experimentation 145

Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. They cannot process language inputs generally. See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

IBM Big Data Hub

MAY 17, 2024

” Given the statistics—82% of surveyed respondents in a 2023 Statista study cited managing cloud spend as a significant challenge—it’s a legitimate concern. Cloud maturity models (or CMMs) are frameworks for evaluating an organization’s cloud adoption readiness on both a macro and individual service level.

Modeling 96

Modeling Cost-Benefit Optimization Digital Transformation 96

CIO Business Intelligence

JANUARY 19, 2024

The US Bureau of Labor Statistics (BLS) forecasts employment of data scientists will grow 35% from 2022 to 2032, with about 17,000 openings projected on average each year. You need experience in machine learning and predictive modeling techniques, including their use with big, distributed, and in-memory data sets.

Data Science 126

Data Science Machine Learning Predictive Modeling Forecasting 126

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

CIO Business Intelligence

JUNE 14, 2023

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. They can also transform the data, create data models, visualize data, and share assets by using Power BI.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

CIO Business Intelligence

JULY 31, 2024

This year, however, Salesforce has accelerated its agenda, integrating much of its recent work with large language models (LLMs) and machine learning into a low-code tool called Einstein 1 Studio. Einstein 1 Studio is a set of low-code tools to create, customize, and embed AI models in Salesforce workflows. What is Einstein 1 Studio?

Data Lake 119

Data Lake IT Sales Experimentation 119

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

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

CIO Business Intelligence

SEPTEMBER 30, 2024

AI ‘bake-offs’ under way Mathematica’s PaaS has not yet implemented AI models in production, but Bell grasps the power of machine learning (ML) and generative AI to uncover new insights that will help Mathematica’s clients.

Data Quality 116

Data Quality Experimentation Insurance Statistics 116

CIO Business Intelligence

MARCH 21, 2022

The more high-quality data available to data scientists, the more parameters they can include in a given model, and the more data they will have on hand for training their models. It doesn’t conform to a data model but does have associated metadata that can be used to group it. Semi-structured data falls between the two.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Data Science 137

FineReport

DECEMBER 11, 2019

. – Head First Data Analysis: A learner’s guide to big numbers, statistics, and good decisions. The big news is that we no longer need to be proficient in math or statistics, or even rely on expensive modeling software to analyze customers. By Michael Milton. – Data Divination: Big Data Strategies.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

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. What comes first: A new brand or operating model?

IT 136

IT Digital Transformation Broadcasting Business Objectives 136

Occam's Razor

APRIL 8, 2013

Let's listen in as Alistair discusses the lean analytics model… The Lean Analytics Cycle is a simple, four-step process that shows you how to improve a part of your business. Another way to find the metric you want to change is to look at your business model. The business model also tells you what the metric should be.

Metrics 157

Metrics KPI Analytics Key Performance Indicator 157

Occam's Razor

SEPTEMBER 19, 2011

How do you get over the frustration of having done attribution modeling and realizing that it is not even remotely the solution to your challenge of using multiple media channels? You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. ask for a raise. It is that simple.

Measurement 120

Measurement Advertising Testing Marketing 120

CIO Business Intelligence

AUGUST 21, 2024

According to Gartner, an agent doesn’t have to be an AI model. Starting in 2018, the agency used agents, in the form of Raspberry PI computers running biologically-inspired neural networks and time series models, as the foundation of a cooperative network of sensors. “It And, yes, enterprises are already deploying them.

Risk 136

Risk Insurance Cost-Benefit Software 136

CIO Business Intelligence

JANUARY 2, 2024

He plans to scale his company’s experimental generative AI initiatives “and evolve into an AI-native enterprise” in 2024. It involves reimagining our strategies, business models, processes and culture centered around AI’s capabilities, to reshape how we work and drive unparalleled productivity and innovation,” he says. “AI

IT 144

IT Cost-Benefit Statistics Consulting 144

DataRobot Blog

JANUARY 17, 2023

Recently, a prospective customer asked me how I reconcile the fact that DataRobot has multiple very successful investment banks using DataRobot to enhance the P&L of their trading businesses with my comments that machine learning models aren’t always great at predicting financial asset prices. For price discovery (e.g.,

Machine Learning 87

Machine Learning Marketing Experimentation Data-driven 87

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. I was very comfortable with that definition, having spent my PhD years on several predictive modelling tasks, and having worked as a software engineer prior to that.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

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

CIO Business Intelligence

JUNE 28, 2023

Belcorp operates under a direct sales model in 14 countries. As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.

Digital Transformation 105

Digital Transformation Cost-Benefit Informatics Data mining 105

Ontotext

FEBRUARY 14, 2024

Ivory tower modeling We’ve seen too many models developed by isolated ontologists that don’t survive the first battle with the data. There’s a famous saying by a statistician, George Box, “All models are wrong, but some are useful.” ” So, how do you know whether your model is useful?

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

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. Data Analysis Libraries.

Knowledge Discovery 98

Knowledge Discovery Visualization Data mining Reporting 98

Domino Data Lab

JANUARY 24, 2019

These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. These tools support a breadth of use cases including data science, data engineering, and model operations. Proprietary (often GUI-driven) data science platforms.

Data Science 65

Data Science Marketing Data-driven Statistics 65

DataRobot Blog

JULY 28, 2022

Companies are emphasizing the accuracy of machine learning models while at the same time focusing on cost reduction, both of which are important. In addition to the accuracy of the models we built, we had to consider business metrics, cost, interpretability, and suitability for ongoing operations. Sensor Data Analysis Examples.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. However, the downside of using a larger unit of randomization is that we lose experimental power. We now describe a generative model for how effects might propagate through the network.

Testing 59

Testing Experimentation Measurement Modeling 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. The challenge has been figuring out what to do once the model is built.

Machine Learning 56

Machine Learning IoT Experimentation Statistics 56

Business Over Broadway

FEBRUARY 21, 2021

As such, data science requires three broad skill sets , including subject matter expertise, statistics/math and technology/programming. Experimentation and iteration to improve existing ML models (25%). The practice of data science is about extracting value from data to help inform decision making and improve algorithms.

Machine Learning 60

Machine Learning Data Science Statistics Experimentation 60

The Unofficial Google Data Science Blog

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. by DANIEL PERCIVAL Randomized experiments are invaluable in making product decisions, including on mobile apps.

Experimentation 62

Experimentation Measurement Modeling Statistics 62

Occam's Razor

JUNE 6, 2011

So in addition to becoming good at Omniture, Google Analytics, Baidu Analytics , pick one other tool from the Experimentation, Voice of Customer, Competitive Intelligence buckets of Web Analytics 2.0. Get an overall web analytics strategy book, one that covers the ecosystem, the mental models to apply, key analytics techniques.

Analytics 126

Analytics Consulting Statistics Marketing 126

DataRobot Blog

OCTOBER 27, 2022

While changes in new data can threaten the performance of production models, data drift can be a strategic opportunity for your AI solution to quickly adapt to new patterns and maintain competitive advantage over not-so-quick competitors. Drill Down into Drift for Rapid Model Diagnostics. Change is inevitable. Growth is optional.

Data Science 52

Data Science Experimentation Visualization Data-driven 52

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. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

Cost-Benefit 89

Cost-Benefit Manufacturing Modeling Interactive 89

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’s easy to deploy, monitor, and manage models in production and react to changing conditions. And any predictive model can become an AI app in minutes—no coding required.

ROI 52

ROI Machine Learning Experimentation Data Science 52

Domino Data Lab

OCTOBER 2, 2019

As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences. You can use a regression model. model = OLS(X['Y'], X[['D', 'intercept']]) result = model.fit() result.summary(). A complementary Domino project is available. . Introduction. Why did this work?

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. Continuous pipeline monitoring with SPC (statistical process control). What DataOps best practices put you on track to achieving this ideal? Let’s take a look. Results (i.e.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

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 45

Statistics Optimization Modeling Experimentation 45