DataRobot Blog

DECEMBER 6, 2022

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Bureau of Labor Statistics predicts that the employment of data scientists will grow 36 percent by 2031, 1 much faster than the average for all occupations. Read the blog. Read the blog.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. For any given input, the same program won’t necessarily produce the same output; the output depends entirely on how the model was trained.

Management 332

Management Machine Learning Experimentation Metrics 332

Sisense

SEPTEMBER 3, 2020

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Business Analyst.

Statistics 97

Statistics Metrics Visualization Finance 97

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Galbraith eventually opted to publish Cuckoo’s Calling through an acquaintance of sorts. but the publishing industry failed to see it. The AIgent was built with BERT, Google’s state-of-the-art language model.

Modeling 59

Modeling Metadata Publishing Sales 59

Cloudera

DECEMBER 3, 2021

In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI. For example, training an interview screening model using education data often contains gender information. As discussed in this article , model design can also be a source of bias too. Model Drift. Governance.

Uncertainty 98

Uncertainty Measurement Metrics Risk 98

Analytics Vidhya

NOVEMBER 9, 2020

This article was published as a part of the Data Science Blogathon. Overview Challenges if we use the Linear Regression model to solve a. The post Binary Cross Entropy aka Log Loss-The cost function used in Logistic Regression appeared first on Analytics Vidhya.

Data Science 166

Data Science Publishing Modeling Analytics 166

IBM Big Data Hub

AUGUST 7, 2023

2,3 When clinical trials are prematurely discontinued due to trial site underperformance, the research questions remain unanswered and research findings end up not published. AI algorithms have the potential to surpass traditional statistical approaches for analyzing comprehensive recruitment data and accurately forecasting enrollment rates.

Optimization 59

Optimization Forecasting Data-driven Strategy 59

Octopai

OCTOBER 26, 2022

Continuous pipeline monitoring with SPC (statistical process control). products or product components) are checked to make sure that they do not deviate in a statistically significant way from the expected results. Continuous DataOps metrics testing checks data’s validity, completeness and integrity at input and output.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

Cloudera

FEBRUARY 25, 2021

AMPs move the starting line for any ML project by enabling data scientists to start with a full end-to-end project developed for a similar use case, including a trained and deployed ML model, as well as prebuilt predictive business applications, out of the box. The work of a machine learning model developer is highly complex.

Deep Learning 82

Deep Learning Machine Learning Forecasting Visualization 82

IBM Big Data Hub

AUGUST 11, 2023

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. It advances the scalability of ML in real-world applications by using algorithms to improve model performance and reproducibility. Where they are deployed.

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

Cloudera

JUNE 28, 2022

This blog will be published in two parts. For each transaction, NiFi makes a call to a production model in Cloudera Machine Learning (CML) to score the fraud potential of the transaction. A dashboard feeds from the Kudu database to show fraud summary statistics. This is what we call the first-mile problem.

Dashboards 81

Dashboards Machine Learning Statistics KPI 81

Smarten

NOVEMBER 19, 2018

Predictive analytics employs various analytical and modeling techniques, leveraging historical data and business results to identify crucial relationships, opportunities and risks so that business managers can more accurately predict growth, and competitive and market changes and identify trends and patterns. What is Predictive Analytics?

Business Objectives 40

Business Objectives Predictive Analytics Analytics Forecasting 40

Sisense

MARCH 4, 2019

The power of the Hunch system lies in the DNN training process, which ultimately yields the query approximation model. In order to do this, the system first extracts statistical values (data distribution) from the raw data. Once the training is finalized, we use a set of accuracy metrics to evaluate the model’s approximation.

Big Data 68

Big Data Data Analytics Analytics IoT 68

Smarten

NOVEMBER 22, 2017

Self-Serve Advanced Analytics provides a roadmap to guide you through a maze of organizational data with tools and models that will bring together data from disparate sources and enable sophisticated yet simple analytics. These tools allow the enterprise to establish and monitor key metrics and to objectively assess results.

Analytics 40

Analytics IT Business Intelligence Visualization 40

The Unofficial Google Data Science Blog

SEPTEMBER 23, 2015

by KAY BRODERSEN For the first time in the history of statistics, recent innovations in big data might allow us to estimate fine-grained causal effects, automatically and at scale. How can we predict the benefits the idea's realization would create for users, publishers, developers, or advertisers? Let's take a look at both challenges.

Advertising 47

Advertising Statistics Big Data Modeling 47

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. The process of creating models is called modeling.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). in next several years.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

JULY 22, 2019

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” He was saying this doesn’t belong just in statistics. Key highlights from the session include. Transcript. Tukey did this paper.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

AWS Big Data

JUNE 28, 2023

The company’s business analysts want to generate metrics to identify ticket movement over time, success rates for sellers, and the best-selling events, venues, and seasons. They would like to get these metrics in near-real time using a zero-ETL integration. model) in Amazon Aurora MySQL-Compatible edition.

Data Warehouse 83

Data Warehouse Analytics Metrics Dashboards 83

O'Reilly on Data

MAY 27, 2020

Ever since 1989, the state has periodically published a report card that rates each surgeon, by name, based on how many of that surgeon’s patients died in hospital or within 30 days after coronary artery bypass surgery. There are about 150 cardiac surgeons in New York State, for instance.

Risk 355

Risk Testing Reporting Measurement 355

Peter James Thomas

AUGUST 12, 2018

These normally appear at the end of an article, but it seemed to make sense to start with them in this case: Recently I published Building Momentum – How to begin becoming a Data-driven Organisation. Pertinence and fidelity of metrics developed from Data. There are often compromises to be made in defining metrics.

Metrics 49

Metrics Statistics Data Quality Measurement 49

AWS Big Data

MARCH 21, 2024

The company’s business analysts want to generate metrics to identify ticket movement over time, success rates for sellers, and the best-selling events, venues, and seasons. They would like to get these metrics in near real time using a zero-ETL integration.

Data Warehouse 86

Data Warehouse Metrics Statistics Optimization 86

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. We will follow the example of Janson and Olsson , and start from this generalized definition of the metric, which they call iota.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

DataKitchen

OCTOBER 11, 2021

The automated orchestration published the data to an AWS S3 Data Lake. Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. Statistical Process Control – applies statistical methods to control a process. Data Completeness – check for missing data.

Testing 157

Testing Metadata Dashboards Statistics 157

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

DataRobot

NOVEMBER 9, 2021

“One look is worth a Thousand Words” This phrase was used in 1913 to convey that graphics had a place in newspaper publishing. There are some interesting insights in adding unstructured features to the models. The dataset also provides centralized and windowed statistics features.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

Occam's Razor

JUNE 8, 2006

You have a inherent ability to hear people and their problems and all the while in your brain you are thinking of 10 interesting ways in which you can slice the Site Overlay or other clickstream metrics to validate. If I get enough different ones I’ll create a new list and publish that (with due credit to you). You guys rock!!).

KPI 94

KPI Measurement Statistics Metrics 94

IBM Big Data Hub

JULY 28, 2022

To take full advantage metadata enrichments, you’ll want to publish data assets, enriched with metadata‚ to a catalog where users can quickly find and access the right information. The workflow and user experience for discovering, enriching and publishing a large number of data assets from a source to a catalog has been entirely revisited.

Metadata 57

Metadata Data Quality Machine Learning Statistics 57

Andrew White

JANUARY 9, 2022

I try to relate as much published research as I can in the time available to draft a response. We will publish a new Top Trends for D&A for 2022 in a couple of months. This was not statistic and we have not really explored this in any greater detail since. I hope they are helpful. Keep a watch on Gartner.com.

Analytics 64

Analytics Measurement Modeling Data-driven 64

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. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

MARCH 3, 2019

We had big surprises at several turns and have subsequently published a series of reports. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). What’s been the impact of using ML models on culture and organization? Who builds their models?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Insight

FEBRUARY 19, 2020

My goal here is not to improve upon the current prediction algorithms but rather to describe a model I devised, called ReelRisk , that uses random resampling to generate a range of predictions which can then be used as a risk assessment tool to determine early on whether to fund a movie. The solid line shows the y=x line for comparison.

Risk 67

Risk ROI Modeling Metrics 67

AWS Big Data

SEPTEMBER 13, 2023

A modern data architecture is an evolutionary architecture pattern designed to integrate a data lake, data warehouse, and purpose-built stores with a unified governance model. In the Table statistics section, you will see an output similar to the following screenshot.

Data Lake 90

Data Lake Data Processing Metadata Snapshot 90

datapine

AUGUST 29, 2022

And with that understanding, you’ll be able to tap into the potential of data analysis to create strategic advantages, exploit your metrics to shape them into stunning business dashboards , and identify new opportunities or at least participate in the process. Product/market fit is THE most important factor to get right.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

O'Reilly on Data

OCTOBER 18, 2022

What if a named entity recognition (NER) system, based on a cutting-edge large language model (LLM), fails for Chinese, Cyrillic, or Arabic text? Audit has been an integral aspect of model risk management (MRM) in consumer finance for years, and colleagues at BLDS and QuantUniversity have been conducting model audits for some time.

Risk Management 291

Risk Management Risk Testing Measurement 291

CIO Business Intelligence

AUGUST 5, 2022

Today, data visualization encompasses all manners of presenting data visually, from dashboards to reports, statistical graphs, heat maps, plots, infographics, and more. Choropleths use shades or patterns on a map to represent a statistical variable, such as population density by state. What is the business value of data visualization?

Visualization 118

Visualization Dashboards Statistics Business Intelligence 118

The Unofficial Google Data Science Blog

APRIL 16, 2019

In fact, this blog has published posts on this very topic. Modern products often don't start with set-in-stone business models because starting and scaling costs are low. Such decisions involve an actual hypothesis test on specific metrics (e.g. The metrics to measure the impact of the change might not yet be established.

Experimentation 68

Experimentation Sales Metrics Measurement 68