Occam's Razor

APRIL 8, 2013

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This should not be news to you. But it is not routine.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

CIO Business Intelligence

APRIL 12, 2024

Computer Vision also gives insights about customer traffic in stores, including metrics on conversion rate and sales numbers, thus helping the company decide what products to stock, how to display them, and how to arrange products across the store, Allison adds. Tractor Supply Co. It makes the team member much more efficient.”

Experimentation 116

Experimentation Sales Digital Transformation Metrics 116

CIO Business Intelligence

FEBRUARY 21, 2024

For example, top researchers at Florida State University are now developing innovative large language models (LLMs) to help advance research in areas like material science and healthcare — going beyond gen AI used by the general public. We developed a model to predict student outcomes based on metrics from historical evidence,” he says. “We

Recreation/Entertainment 135

Recreation/Entertainment Experimentation Modeling Data-driven 135

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g.,

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

IBM Big Data Hub

DECEMBER 4, 2023

And the abundance of data available for training models has opened up vast possibilities for experimentation and learning. Evaluating the performance of generative AI models ensures that they meet desired standards and can provide reliable outputs.

Experimentation 67

Experimentation Modeling Metrics Machine Learning 67

CIO Business Intelligence

APRIL 2, 2024

Joanne Friedman, PhD, CEO, and principal of smart manufacturing at Connektedminds, says orchestrating success in digital transformation requires a symphony of integration across disciplines : “CIOs face the challenge of harmonizing diverse disciplines like design thinking, product management, agile methodologies, and data science experimentation.

Management 140

Management Digital Transformation Testing Experimentation 140

AWS Big Data

AUGUST 21, 2023

For the demo, we’re using the Amazon Titan foundation model hosted on Amazon Bedrock for embeddings, with no fine tuning. It similarly codes the query as a vector and then uses a distance metric to find nearby vectors in the multi-dimensional space. OpenSearch Service supports a variety of search and relevance ranking techniques.

Data Processing 89

Data Processing Experimentation Visualization Metrics 89

IBM Big Data Hub

MARCH 4, 2024

Figure 2: ROI potential by transforming into an AI+ enterprise Organizations with high data maturity that embed an AI+ transformation model into the enterprise fabric and culture can generate up to 2.6 Consider the following: Do you need a public foundation model? times higher ROI. Often, this decision is made too quickly.

Enterprise 107

Enterprise ROI Risk Strategy 107

DataRobot Blog

OCTOBER 18, 2022

Monitoring and Managing AI Projects with Model Observability. Model Observability – the ability to track key health and service metrics for models in production – remains a top priority for AI-enabled organizations. DataRobot Booth at Big Data & AI Toronto 2022.

Big Data 96

Big Data Forecasting Experimentation Metrics 96

CIO Business Intelligence

NOVEMBER 13, 2023

So if you are seeking to lead transformational change at your organization, it’s worth knowing the 10 most common reasons why digital transformation fails and what you as an IT leader can learn from those failures. Without a clear understanding of what their digital transformation should achieve, it’s easy for companies to get lost in the weeds.

Digital Transformation 143

Digital Transformation Cost-Benefit Experimentation Measurement 143

DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. They can enjoy a hosted experience with code snippets, versioning, and simple environment management for rapid AI experimentation.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

O'Reilly on Data

APRIL 19, 2021

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. It’s possible that pandemic-induced boredom led more people to respond, but we doubt it. Executive Summary. Respondents.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

Sisense

MARCH 6, 2019

Gartner chose to group the rest of the keynote into three main messages according to the following categories: Here are some of the highlights as presented for each of them: Data Driven – “Adopt an Experimental Mindset”. At Sisense we’ve been preaching for BI prototyping and experimentation for quite a while now.

Data Analytics 75

Data Analytics Analytics Experimentation Data-driven 75

DataRobot

JUNE 10, 2021

Traditionally, experimentation and observation was the only way to understand the physical-chemical properties of the molecule. DataRobot’s AutoML uses different feature engineering techniques and a variety of machine learning algorithms to identify the best model for multilabel classification. With DataRobot this is a one-click step.

Metrics 52

Metrics Machine Learning Visualization Experimentation 52

AWS Big Data

JANUARY 9, 2024

Traditional lexical search, based on term frequency models like BM25, is widely used and effective for many search applications. Semantic search In semantic search, the search engine uses an ML model to encode text or other media (such as images and videos) from the source documents as a dense vector in a high-dimensional vector space.

Cost-Benefit 89

Cost-Benefit Visualization Modeling Machine Learning 89

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. This integration is key in assuring that models evolve with the data – to avoid, for example, model drift. And this blog will focus on Predictive Analytics. Here are the key stages: .

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Features: DistilBERT Text Embeddings Once I had a raw dataset, I could begin engineering features and building a natural language processing (NLP) model.

Modeling 59

Modeling Metadata Publishing Sales 59

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

CIO Business Intelligence

MAY 16, 2023

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. AI Opportunities Generative AI is the basis for sophisticated AI models such as ChatGPT and Dall-E. Conversations suggest that AI is already transforming most major industries.

Advertising 105

Advertising Recreation/Entertainment Experimentation Enterprise 105

Occam's Razor

MAY 22, 2006

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

Experimentation 103

Experimentation Testing Optimization Measurement 103

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation. It is fast and slow.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

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. Data scientist job description.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Structured Data 137

Domino Data Lab

DECEMBER 4, 2020

Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Occam's Razor

JULY 24, 2018

work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps , metrics ladders of awesomeness , and… so… much… more. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more.

Analytics 93

Analytics Metrics Strategy Measurement 93

Domino Data Lab

AUGUST 6, 2021

When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Develop: includes accessing and preparing data and algorithms, researching and development of models and experimentation. 4 Steps in the DSLC. Assess the Project.

Data Science 75

Data Science Testing Modeling Publishing 75

DataRobot

JUNE 23, 2021

As they strive to improve models, data scientists continually try new approaches to refine their predictions. After adding the preferred code, teams can take advantage of the existing DataRobot capabilities, such as metrics, explainability, visualizations, deployment, monitoring, collaboration, and governance. So let’s dig in!

Machine Learning 52

Machine Learning Modeling Experimentation Visualization 52

DataKitchen

OCTOBER 5, 2021

DataOps enables: Rapid experimentation and innovation for the fastest delivery of new insights to customers. The data scientist’s products are models and segmentations. We find it helpful to think of data operations as a factory. Most organizations run the data factory using manual labor. What is DataOps. Low error rates.

Testing 157

Testing Dashboards Measurement Experimentation 157

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

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. This simulation is based on the actual user network of GCP.

Testing 58

Testing Experimentation Measurement Modeling 58

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. Capabilities Beyond Classic Jupyter for End-to-end Experimentation. Auto-scale compute.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Occam's Razor

MARCH 23, 2021

Data Warehousing. And, a thousand other silos (depending on your company size). One outcome of this reality is that while every team works hard to do their very best work, it is rare that they earn strategic influence from their work. That’s not really surprising, if your view of your scope is narrow… Your impact will be narrow as well.

Analytics 131

Analytics Scorecard Strategy Testing 131

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. Key To Your Digital Success: Web Analytics Measurement Model. Web Data Quality: A 6 Step Process To Evolve Your Mental Model. "Engagement" Is Not A Metric, It's An Excuse. How do I choose well? How to focus?"

KPI 124

KPI Metrics Measurement ROI 124

CIO Business Intelligence

JUNE 27, 2022

Tyson: You’re coming up to two years since you added Sentry’s ability to monitor for performance which has some pretty fine-grained metrics in terms of identifying where bottlenecks are located in code. It was easier for me to build Sentry in open source than to try to make an existing product work. Do you still code as a CTO?

Software 115

Software Experimentation Testing Marketing 115

AWS Big Data

MAY 2, 2023

If your updates to a dataset triggers multiple subsequent DAGs, then you can use the Airflow metric max_active_tasks_per_dag to control the parallelism of the consumer DAG and reduce the chance of overloading the system. Removal of experimental Smart Sensors. Amazon MWAA supports multiple versions of Apache Airflow (v1.10.12, v2.0.2,

Testing 98

Testing Experimentation Management Metadata 98

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 DataOps metrics testing checks data’s validity, completeness and integrity at input and output. But data, at best, can only be a powerful vehicle, or a reliable GPS system.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52