Occam's Razor

AUGUST 9, 2021

Almost all metrics you currently use have one common thread: They are almost all backward-looking. If you want to deepen the influence of data in your organization – and your personal influence – 30% of your analytics efforts should be centered around the use of forward-looking metrics. Predictive metrics!

Metrics 142

Metrics Analytics Key Performance Indicator Predictive Modeling 142

Cloudera

SEPTEMBER 20, 2022

This blog is intended to serve as an ethics sheet for the task of AI-assisted comic book art generation, inspired by “ Ethics Sheets for AI Tasks.” AI-assisted comic book art generation is a task I proposed in a blog post I authored on behalf of my employer, Cloudera. Introduction. Scope, motivation, and benefits.

Deep Learning 87

Deep Learning Cost-Benefit Risk Data-driven 87

Occam's Razor

MAY 2, 2017

There is no golden metric for everyone, we are all unique snowflakes! :). and tell you what are the best key performance indicators (metrics) for them. In the past I’ve shared a cluster of metrics that small, medium and large businesses can use as a springboard…. If you want to play along. Don’t read what I’ve chosen.

Metrics 141

Metrics Key Performance Indicator B2B Measurement 141

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 361

Marketing Experimentation Metrics Testing 361

CIO Business Intelligence

MAY 22, 2024

Close behind were machine learning and natural language processing. Gartner recommends CIOs thinking about deploying AI to first consider potential use cases , establish metrics to measure value, and run pilot programs before launching large-scale projects.

ROI 143

ROI IT Measurement Cost-Benefit 143

CIO Business Intelligence

APRIL 8, 2024

In the years since author Michael Lewis popularized sabermetrics in his 2003 book, Moneyball: The Art of Winning an Unfair Game , sports analytics has evolved considerably beyond baseball. Computer vision, AI, and machine learning (ML) all now play a role. Capel-Davies’ advice: Focus on communication.

Analytics 102

Analytics Broadcasting Predictive Analytics Metrics 102

Jet Global

NOVEMBER 15, 2021

A Tax Key Performance Indicator (KPI) or metric is a clearly defined quantifiable measure that an organization, or business, uses to measure the success of its Tax Function over time. Since every organization has its own manner of operation, the KPIs or metrics used for tax will vary from one organization to another.

Metrics 52

Metrics Reporting KPI Risk Management 52

DataRobot

FEBRUARY 10, 2021

Machine learning is a glass cannon. The promise and power of AI lead many researchers to gloss over the ways in which things can go wrong when building and operationalizing machine learning models. As a data scientist, one of my passions is to reproduce research papers as a learning exercise.

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

Analytics Vidhya

AUGUST 6, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Hello folks, so this article has the detailed concept of. The post How KNN Uses Distance Measures? appeared first on Analytics Vidhya.

Measurement 322

Measurement Data Science Publishing Analytics 322

Smart Data Collective

NOVEMBER 28, 2022

Business leaders can then consolidate whatever performance metrics matter most to them into dedicated interactive dashboards and dynamic reports. For example, a service business owner can examine their appointment conversion data to check which services are the most popular or how many customers follow through with each booking.

Business Analytics 105

Business Analytics Analytics Business Intelligence Sales 105

O'Reilly on Data

JANUARY 25, 2021

This study is based on title usage on O’Reilly online learning. The data includes all usage of our platform, not just content that O’Reilly has published, and certainly not just books. More traditional modes also saw increases: usage of books increased by 11%, while videos were up 24%. AI, Machine Learning, and Data.

Machine Learning 362

Machine Learning Software Testing Technology 362

IBM Big Data Hub

FEBRUARY 12, 2024

And thanks to online metrics, specific customer feedback, and data analytics, these retailers had more information about their customers than ever before. Look at Amazon, which started with books and moved into virtually everything else. Increasingly organizations expanded what they offered.

Data-driven 96

Data-driven Manufacturing Consulting Advertising 96

CIO Business Intelligence

FEBRUARY 21, 2024

One is knowledge of the emerging mega trends in technology — data, AI, and machine learning — and the other is understanding organizational culture needed to advance the technology goals and to inspire contributors,” he says. We developed a model to predict student outcomes based on metrics from historical evidence,” he says. “We

Recreation/Entertainment 130

Recreation/Entertainment Experimentation Modeling Data-driven 130

Cloudera

MARCH 18, 2021

The Fundamental Review of the Trading Book (FRTB), introduced by the Basel Committee on Banking Supervision (BCBS), will transform how banks measure risk. A revised boundary between the trading book and banking book. A machine learning ops framework that supports regular backtesting and P&L on attribution testing.

Risk 55

Risk Machine Learning Risk Management Measurement 55

O'Reilly on Data

MAY 11, 2020

It predates recommendation engines, social media, engagement metrics, and the recent explosion of AI, but not by much. The Entertainment” is not the result of algorithms, business incentives and product managers optimizing for engagement metrics. And like a lot of near-future SciFi, it’s remarkably prescient.

Modeling 188

Modeling Advertising Recreation/Entertainment Metrics 188

Juice Analytics

MAY 14, 2021

We can’t climb our way out of these problems by always betting on the machines to do more. For all the advances in artificial intelligence and machine learning, it seems to me that the people-side of analytics continues to be neglected. I’m not talking about hiring more data scientists or applying machine learning models.

Data-driven 74

Data-driven Visualization Machine Learning Metrics 74

IBM Big Data Hub

FEBRUARY 13, 2024

Optimize performance through automation Turbonomic revolutionizes application performance optimization by leveraging AI and machine learning algorithms to analyze real-time performance data and give insight into application response time and transaction time.

Optimization 67

Optimization Metrics Machine Learning Dashboards 67

CIO Business Intelligence

MAY 29, 2024

Sundberg knows about as much as anybody on Scope 3 reporting and covers the subject in his book Sustainable IT playbook for technology leaders. Even though large companies have been working with machine learning for quite some time now, their models aren’t as sophisticated as the larger open-source models, says Sundberg.

Cost-Benefit 69

Cost-Benefit Measurement Reporting Modeling 69

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. Monitoring tools track performance metrics such as latency, throughput, and error rates. What is an ETL pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. To better understand this, imagine a chatbot that helps travelers book their travel. However, the value of such important data diminishes significantly over time.

Data Lake 92

Data Lake Unstructured Data Management Modeling 92

Cloudera

DECEMBER 3, 2021

There are a number of metrics that can be used to measure the performance of a system; they include accuracy, precision and F-score to name only three. Tracking key metrics and statistical distributions over time and alerting humans when either of these significantly drift can ensure that systems remain performant and fair.

Uncertainty 100

Uncertainty Measurement Metrics Risk 100

CIO Business Intelligence

NOVEMBER 29, 2022

High-value data products can have board-level KPIs and metrics associated with them. Data consumers (analytics teams and developers, for example) then generate insights and business value from analytics, machine learning, and AI. It’s the foundational architecture and data integration capability for high-value data products.

Data Architecture 103

Data Architecture Data Integration IoT Strategy 103

Alation

FEBRUARY 20, 2020

Finding the right data asset in a modern enterprise can be like trying to find a book in a massive library. In the 20th century, libraries leveraged card catalogs to make it easier for book seekers to search by title, author, or category. vs. Machine-Learning. Inventory vs. Catalog. Context is King.

Metrics 52

Metrics Consulting Machine Learning Statistics 52

Edwin Chen

OCTOBER 6, 2014

Click-through rate may be your initial hope…but after a bit of thought, it's not clear that it's the best metric after all. Metrics like CTR, or even number of favorites and retweets, will probably optimize for showing quick one-liners and pictures of funny cats. Amazon, and Moving Beyond Log-Based Metrics.

Metrics 79

Metrics Measurement Machine Learning Testing 79

datapine

MAY 17, 2021

Without a doubt, the best way to drive maximum value from the metrics, insights, and information is through something called data discovery. First of all, you can track your business performance thanks to specific metrics – Key Performance Indicators – and get all the insight that your data has to offer. click to enlarge**.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161

IBM Big Data Hub

APRIL 8, 2024

Answer: To share feedback on the product and learn more about new features or technical updates planned for Amazon RDS for Db2, connect with your IBM or AWS representative to  book a meeting with IBM Db2 experts.  Can Amazon RDS for Db2 be used for running data warehousing workloads?

Snapshot 71

Snapshot Data Warehouse Data Processing Management 71

Sisense

APRIL 10, 2020

We are surrounded by written text every day: emails, SMS messages, webpages, books, and much more. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. There are many performance metrics to evaluate performance of Machine Learning models.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Domino Data Lab

MAY 8, 2019

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. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

DataRobot

APRIL 13, 2021

Market Metrics says that the probability of selling to an existing customer is in the 60-70 percent range, but only in the 15-20 percent range for a new prospect. Before you launch the campaign, use DataRobot to train a machine learning churn model , using historical data on those customers who have churned in the past.

Forecasting 52

Forecasting Metrics Machine Learning Sales 52

Occam's Razor

MARCH 23, 2021

A key part of how this manifested in our work was doing truly super-advanced machine-learning powered analysis to answer hard questions that few can successfully. The benchmark for the beautiful metric AVOC is 15.3%. Now repeat this across many, many metrics, for many dimensions, in the three clusters you see above.

Analytics 131

Analytics Scorecard Strategy Testing 131

Alation

MAY 24, 2022

Some vendors leverage machine learning to build rules where others rely on manually declared rules. Metrics — Profile data to provide a sense of it, such as checking for nulls and blanks to ensure data exists. Learn more about the Open Data Quality Initiative by exploring the resources below. Book a demo today.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

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

Domino Data Lab

JUNE 2, 2019

Secondly, I talked backstage with Michelle, who got into the field by working on machine learning projects, though recently she led data infrastructure supporting data science teams. Just doing machine learning is not enough, and sometimes not even necessary.”. First off, her slides are fantastic! Nick Elprin.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

Data Science 101

OCTOBER 31, 2019

A definition from the book ‘Data Mining: Practical Machine Learning Tools and Techniques’, written by, Ian Witten and Eibe Frank describes Data mining as follows: “ Data mining is the extraction of implicit, previously unknown, and potentially useful information from data.

Data mining 77

Data mining KDD Data Science Forecasting 77

datapine

OCTOBER 21, 2020

Then, they could use machine learning to find the most accurate algorithms that predicted future admissions trends. Leveraging analytics tools to track the supply chain performance metrics , and make accurate, data-driven decisions concerning operations as well as spending can save hospitals up to $10 million per year.

Big Data 364

Big Data Data Analytics Analytics Cost-Benefit 364

Domino Data Lab

OCTOBER 9, 2019

Clustering is a machine learning technique that enables researchers and data scientists to partition and segment data. AWP Pearson provided the appropriate permissions to excerpt the book as well as provide a complementary Domino project. It divides the observations into discrete groups based on some distance metric.

Statistics 100

Statistics Machine Learning Metrics Measurement 100

Alation

MAY 2, 2023

The hospitality industry generates vast amounts of data from various sources, including customer bookings, transactions, loyalty programs, social media, and guest feedback. For example, hotels can use data analytics to identify booking patterns and optimize room rates, inventory, and staffing levels.

Data Analytics 52

Data Analytics Analytics ROI Data Governance 52