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

CIO Business Intelligence

OCTOBER 24, 2024

By 2026, hyperscalers will have spent more on AI-optimized servers than they will have spent on any other server until then, Lovelock predicts. In some cases, the AI add-ons will be subscription models, like Microsoft Copilot, and sometimes, they will be free, like Salesforce Einstein, he says. growth in device spending.

IT 133

IT Data-driven Recreation/Entertainment Experimentation 133

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 363

Management Machine Learning Experimentation Metrics 363

CIO Business Intelligence

JANUARY 21, 2025

With traditional OCR and AI models, you might get 60% straight-through processing, 70% if youre lucky, but now generative AI solves all of the edge cases, and your processing rates go up to 99%, Beckley says. Even simple use cases had exceptions requiring business process outsourcing (BPO) or internal data processing teams to manage.

Cost-Benefit 142

Cost-Benefit Data-driven Strategy Marketing 142

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Outputs from trained AI models include numbers (continuous or discrete), categories or classes (e.g., spam or not-spam), probabilities, groups/segments, or a sequence (e.g.,

Advertising 357

Advertising Cost-Benefit ROI Machine Learning 357

CIO Business Intelligence

OCTOBER 24, 2024

As they look to operationalize lessons learned through experimentation, they will deliver short-term wins and successfully play the gen AI — and other emerging tech — long game,” Leaver said. Determining the optimal level of autonomy to balance risk and efficiency will challenge business leaders,” Le Clair said.

ROI 127

ROI Data-driven Enterprise Experimentation 127

AWS Big Data

JANUARY 15, 2025

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. This post is co-written by Dr. Leonard Heilig and Meliena Zlotos from EUROGATE. This approach fosters knowledge sharing across the ML lifecycle.

IoT 111

IoT Machine Learning Metadata Data-driven 111

The Unofficial Google Data Science Blog

APRIL 23, 2024

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

AWS Big Data

DECEMBER 12, 2024

Amazon Redshift , optimized for complex queries, provides high-performance columnar storage and massively parallel processing (MPP) architecture, supporting large-scale data processing and advanced SQL capabilities. In this post, we use dbt for data modeling on both Amazon Athena and Amazon Redshift.

Snapshot 95

Snapshot Recreation/Entertainment Experimentation Data Lake 95

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Observe, optimize, and scale enterprise data pipelines. . ModelOp — Governs, monitors, and orchestrates models across the enterprise. Meta-Orchestration .

Testing 300

Testing Machine Learning Consulting Data Science 300

AWS Big Data

NOVEMBER 19, 2024

Customers maintain multiple MWAA environments to separate development stages, optimize resources, manage versions, enhance security, ensure redundancy, customize settings, improve scalability, and facilitate experimentation. micro, remember to monitor its performance using the recommended metrics to maintain optimal operation.

Metadata 111

Metadata Cost-Benefit Metrics Optimization 111

O'Reilly on Data

MARCH 23, 2023

Many of these go slightly (but not very far) beyond your initial expectations: you can ask it to generate a list of terms for search engine optimization, you can ask it to generate a reading list on topics that you’re interested in. It’s important to understand that ChatGPT is not actually a language model. with specialized training.

IT 344

IT Modeling Testing Risk 344

CIO Business Intelligence

MAY 23, 2022

Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex. The cloud is great for experimentation when data sets are smaller and model complexity is light. Potential headaches of DIY on-prem infrastructure.

Experimentation 137

Experimentation Cost-Benefit Data Lake Data Science 137

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. Taking a Multi-Tiered Approach to Model Risk Management. Learn how to leverage Google BigQuery large datasets for large scale Time Series forecasting models in the DataRobot AI platform.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

Cloudera

SEPTEMBER 18, 2024

More and more enterprises are leveraging pre-trained models for various applications, from natural language processing to computer vision. For data providers, InDaiX enhances distribution by reaching Cloudera’s established customer base and provides valuable feedback on data usage and integration with AI models.

Data-driven 91

Data-driven Optimization Experimentation Testing 91

IBM Big Data Hub

MAY 17, 2024

Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan. 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

Occam's Razor

AUGUST 13, 2012

Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. I explain three different models (Online to Store, Across Multiple Devices, Across Digital Channels) and for each I've highlighted: 1. What's possible to measure.

Modeling 125

Modeling Metrics Data Quality Data-driven 125

Ontotext

JULY 24, 2023

I did some research because I wanted to create a basic framework on the intersection between large language models (LLM) and data management. LLM is by its very design a language model. The meaning of the data is the most important component – as the data models are on their way to becoming a commodity.

Modeling 98

Modeling Management Structured Data Data Architecture 98

CIO Business Intelligence

JULY 18, 2024

The early bills for generative AI experimentation are coming in, and many CIOs are finding them more hefty than they’d like — some with only themselves to blame. According to IDC’s “ Generative AI Pricing Models: A Strategic Buying Guide ,” the pricing landscape for generative AI is complicated by “interdependencies across the tech stack.”

Cost-Benefit 136

Cost-Benefit Experimentation Consulting Modeling 136

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. You can start for free with a superb tool: Google's Website Optimizer. So as my tiny gift for you here are five experimentation and testing ideas for you. 3 Optimize the Number of Ads & Layout of Ads. And I meant every word of it.

Testing 113

Testing Experimentation Risk Optimization 113

Domino Data Lab

SEPTEMBER 10, 2020

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant. This means many projects get stuck in endless research and experimentation.

Modeling 101

Modeling Data Science IT Metrics 101

CIO Business Intelligence

MAY 21, 2024

With the generative AI gold rush in full swing, some IT leaders are finding generative AI’s first-wave darlings — large language models (LLMs) — may not be up to snuff for their more promising use cases. With this model, patients get results almost 80% faster than before. It’s fabulous.”

IT 131

IT Cost-Benefit Experimentation Forecasting 131

Occam's Razor

OCTOBER 21, 2013

As an analyst, I was upset that this change would hurt my ability to analyze the effectiveness of my beloved search engine optimization (SEO) efforts – which are really all about finding the right users using optimal content strategies. I've gone through the five stages in the Kubler-Ross model. silverlinings.

Optimization 156

Optimization Reporting Metrics Experimentation 156

CIO Business Intelligence

MAY 9, 2024

Experimentation drives momentum: How do we maximize the value of a given technology? Via experimentation. This can be as simple as a Google Sheet or sharing examples at weekly all-hands meetings Many enterprises do “blameless postmortems” to encourage experimentation without fear of making mistakes and reprisal.

Enterprise 122

Enterprise Experimentation Sales Manufacturing 122

Occam's Razor

SEPTEMBER 13, 2016

Bonus #2: The Askers-Pukers Business Model. Hypothesis development and design of experimentation. Take this as an example… How do you know that this is a profoundly sub-optimal collection of choices to provide? Ok, maybe statistical modeling smells like an analytical skill. Bottom-line. Strategic thinking skills.

Modeling 128

Modeling Reporting Strategy Consulting 128

CIO Business Intelligence

AUGUST 20, 2024

Our mental models of what constitutes a high-performance team have evolved considerably over the past five years. Post-pandemic, high-performance teams excelled at remote and hybrid working models, were more empathetic to individual needs, and leveraged automation to reduce manual work.

IT 141

IT Digital Transformation Experimentation Risk 141

CIO Business Intelligence

OCTOBER 5, 2023

Sandeep Davé knows the value of experimentation as well as anyone. CBRE has also used AI to optimize portfolios for several clients, and recently launched a self-service generative AI product that enables employees to interact with CBRE and external data in a conversational manner. Let’s start with the models.

Experimentation 130

Experimentation Strategy Machine Learning Interactive 130

CIO Business Intelligence

DECEMBER 19, 2024

Additionally, nuclear power companies and energy infrastructure firms are hiring to optimize and secure energy systems, while smart city developers need IoT and AI specialists to build sustainable and connected urban environments, Breckenridge explains.

IT 52

IT IoT Machine Learning Optimization 52

DataRobot Blog

MARCH 8, 2023

Leveraging DataRobot’s JDBC connectors, enterprise teams can work together to train ML models on their data residing in SAP HANA Cloud and SAP Data Warehouse Cloud, as well as have an option to enrich it with data from external data sources.

Enterprise 105

Enterprise Experimentation Machine Learning Data Science 105

Ontotext

JANUARY 10, 2025

Generative AI (GenAI) models, such as GPT-4, offer a promising solution, potentially reducing the dependency on labor-intensive annotation. Through iterative experimentation, we incrementally added new modules refining the prompts. BioRED performance Prompt Model P R F1 Price Latency Generic prompt GPT-4o 72 35 47.8

Informatics 59

Informatics Modeling Metadata Experimentation 59

Rocket-Powered Data Science

OCTOBER 16, 2020

We build models to test our understanding, but these models are not “one and done.” In ML, the learning cycle is sometimes called backpropagation, where the errors (inaccurate predictions) of our models are fed back into adjusting the model’s input parameters in a way that aims to improve the output accuracy.

Digital Transformation 142

Digital Transformation Experimentation Data Science Data Strategy 142

Cloudera

MARCH 2, 2022

You can read previous blog posts on Impala’s performance and querying techniques here – “ New Multithreading Model for Apache Impala ”, “ Keeping Small Queries Fast – Short query optimizations in Apache Impala ” and “ Faster Performance for Selective Queries ”. . Analytical SQL workloads use aggregates and joins heavily.

Data Warehouse 89

Data Warehouse Optimization Analytics Sales 89

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

CIO Business Intelligence

JANUARY 19, 2024

The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more. and SAS Text Analytics, Time Series, Experimentation, and Optimization.

Data Science 126

Data Science Machine Learning Predictive Modeling Forecasting 126

CIO Business Intelligence

NOVEMBER 7, 2022

A developing playbook of best practices for data science teams covers the development process and technologies for building and testing machine learning models. CIOs and CDOs should lead ModelOps and oversee the lifecycle Leaders can review and address issues if the data science teams struggle to develop models.

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

CIO Business Intelligence

MAY 12, 2022

Most tools offer visual programming interfaces that enable users to drag and drop various icons optimized for data analysis. Drag-and-drop Modeler for creating pipelines, IBM integrations. A high level of automation encourages deploying these models into production to generate a constant stream of insights and predictions.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

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