DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Datatron — Automates deployment and monitoring of AI models.

Testing 300

Testing Machine Learning Consulting Data Science 300

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

Management 308

Management Machine Learning Metrics Modeling 308

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

O'Reilly on Data

MARCH 23, 2023

It’s important to understand that ChatGPT is not actually a language model. It’s a convenient user interface built around one specific language model, GPT-3.5, is one of a class of language models that are sometimes called “large language models” (LLMs)—though that term isn’t very helpful. with specialized training.

IT 346

IT Modeling Testing Risk 346

DataKitchen

JANUARY 20, 2021

The center of excellence (COE) model leverages the DataOps team to solve real-world challenges. A COE typically has a full-time staff that focuses on delivering value for customers in an experimentation-driven, iterative, result-oriented, customer-focused way. DataOps Center of Excellence.

Metrics 243

Metrics Experimentation Measurement Testing 243

Domino Data Lab

MAY 15, 2019

Unfortunately, a common challenge that many industry people face includes battling “ the model myth ,” or the perception that because their work includes code and data, their work “should” be treated like software engineering. These steps also reflect the experimental nature of ML product management.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

CIO Business Intelligence

AUGUST 29, 2024

Google has updated its Gemini large language model (LLM) with a new feature, dubbed Gems, that allows users to train Gemini on any topic of their choice and use it as a customized AI assistant for various use cases. and later GPT-4 models become popular. These models include — the smaller Gemini 1.5 Flash model.

Experimentation 128

Experimentation Modeling Enterprise Marketing 128

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

Cloudera

JULY 27, 2021

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. Only through hands-on experimentation can we discern truly useful new algorithmic capabilities from hype.

Data Science 117

Data Science Enterprise Experimentation Machine Learning 117

CIO Business Intelligence

JULY 11, 2023

In recent years, we have witnessed a tidal wave of progress and excitement around large language models (LLMs) such as ChatGPT and GPT-4. In this blog post, we will delve deeper into each of these principles and provide concrete examples to illustrate their importance.

Modeling 95

Modeling Testing Experimentation Risk Management 95

The Unofficial Google Data Science Blog

APRIL 23, 2024

This blog post discusses such a comprehensive approach that is used at Youtube. Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. And we can keep repeating this approach, relying on intuition and luck.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Ontotext

JANUARY 10, 2025

Generative AI (GenAI) models, such as GPT-4, offer a promising solution, potentially reducing the dependency on labor-intensive annotation. This blog post summarizes our findings, focusing on NER as a first-step key task for knowledge extraction. BioRED performance Prompt Model P R F1 Price Latency Generic prompt GPT-4o 72 35 47.8

Informatics 59

Informatics Modeling Metadata Experimentation 59

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

datapine

FEBRUARY 4, 2020

As we have already talked about in our previous blog post on sales reports for daily, weekly or monthly reporting, you need to figure out a couple of things when launching and executing a marketing campaign: are your efforts paying off? 1) Blog Traffic And Blog Leads Report. click to enlarge**. click to enlarge**.

Reporting 280

Reporting Marketing Advertising Metrics 280

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. For the purposes of this blog, we will be creating a new notebook from scratch on the DataRobot platform.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Domino Data Lab

NOVEMBER 14, 2019

Model interpretability continues to spark public discourse among industry. We have covered model interpretability previously, including a proposed definition of machine learning (ML) interpretability. Yet there are tradeoffs to consider when selecting a model. Errors like these may occur when the model is being constructed.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

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

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 63

Modeling Experimentation Knowledge Discovery Testing 63

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

DataRobot

JANUARY 20, 2022

. — Collaborating via Snowflake Data Cloud and DataRobot AI Cloud Platform will enable multiple organizations to build a community movement where experimentation, innovation, and creativity flourish. As ICSs mature digitally, there is a need to ensure that all processes, datasets, and models are transparent and are free from bias. –

Data-driven 145

Data-driven Experimentation Predictive Modeling Data Warehouse 145

The Unofficial Google Data Science Blog

JULY 22, 2020

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

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

Cloudera

APRIL 13, 2021

Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. Proper science takes experimentation and observation, as well as a willingness to accept the failures alongside the successes. Step 7: Maintain the integrity of your models.

Machine Learning 98

Machine Learning Enterprise Experimentation Modeling 98

Domino Data Lab

OCTOBER 14, 2020

We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. For example, our models can show farmers how to increase their production while using less fertilizer.

Experimentation 99

Experimentation Deep Learning Modeling Testing 99

DataRobot Blog

MARCH 16, 2023

Why model-driven AI falls short of delivering value Teams that just focus model performance using model-centric and data-centric ML risk missing the big picture business context. We are also thrilled to share the innovations and capabilities that we have developed at DataRobot to meet and exceed those requirements.

Experimentation 98

Experimentation Data-driven Modeling Metadata 98

DataRobot

MARCH 10, 2021

In this blog post, I will focus on the use of the word autonomous , the dangers of using it with stakeholders, and, in the context of customer experience, the inaccurate perception that all things can be automated, eliminating the need for interactions between employees and customers. Deploy the machine learning model into production.

Machine Learning 145

Machine Learning Experimentation Key Performance Indicator Reporting 145

Cloudera

APRIL 9, 2021

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines. Here are the key stages: .

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

Domino Data Lab

FEBRUARY 12, 2020

this post on the Ray project blog ?. for model serving (experimental), are implemented with Ray internally for its scalable, distributed computing and state management benefits, while providing a domain-specific API for the purposes they serve. Motivations for Ray: Training a Reinforcement Learning (RL) Model.

Experimentation 137

Experimentation Modeling Data Science Machine Learning 137

The Data Visualisation Catalogue

DECEMBER 13, 2021

A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention. User Modeling and User-Adapted Interaction , 16(1), 1–30. Journal of Experimental Psychology: Applied, 4 (2), 119–138. The post New Format for The Bar Chart Reference Page appeared first on The Data Visualisation Catalogue Blog.

Visualization 121

Visualization Experimentation Publishing Interactive 121

IBM Big Data Hub

JUNE 10, 2024

As organizations strive to harness the power of AI while controlling costs, leveraging anything as a service (XaaS) models emerges as a strategic approach. Embracing the power of XaaS XaaS encompasses a broad spectrum of cloud-based and on-premises service models that offer scalable and cost-effective solutions to businesses.

Experimentation 62

Experimentation Optimization Risk Management Machine Learning 62

Dataiku

MARCH 25, 2024

This scenario is not science fiction but a glimpse into the capabilities of Multimodal Large Language Models (M-LLMs), where the convergence of various modalities extends the landscape of AI. But instead, a machine seamlessly identifies the scene and its location, provides a detailed description, and even suggests nearby attractions.

Visualization 132

Visualization Modeling Experimentation Testing 132

datapine

JANUARY 31, 2022

They’re about having the mindset of an experimenter and being willing to let data guide a company’s decision-making process. To do so, the company started by defining the goals, and finding a way to translate employees’ behavior and experience into data, so as to model against actual outcomes.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

DataKitchen

OCTOBER 5, 2021

DataOps enables: Rapid experimentation and innovation for the fastest delivery of new insights to customers. In this blog, we’ll explore the role of the DataOps Engineer in driving the data organization to higher levels of productivity. A more technical discussion will follow in the next edition of this blog series.

Testing 152

Testing Dashboards Measurement Experimentation 152

Domino Data Lab

APRIL 7, 2019

A good NLP library will make it easy to both train your own NLP models and integrate with the downstream ML or DL pipeline. A good NLP library should also implement the latest and greatest algorithms and models – not easy while NLP is having its ImageNet moment and state-of-the-art models are being outpaced twice a month.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

Sisense

JULY 24, 2019

In my previous blog , I wrote about Natural Language Query (NLQ, or search analytics for some), as one of the major topics that we, the AI group in Sisense, are working on. In this blog, I would like to expand on NLQ and discuss how this AI technology can be leveraged in our domain.

Analytics 89

Analytics Experimentation Metadata Big Data 89

CIO Business Intelligence

SEPTEMBER 12, 2022

Prioritize time for experimentation. The team was given time to gather and clean data and experiment with machine learning models,’’ Crowe says. It requires bold bets and a willingness to persevere despite setbacks, criticism, and uncertainty,’’ wrote McKinsey senior partners Laura Furstenthal and Erik Roth in a recent blog post. “By

Experimentation 133

Experimentation Consulting Technology Machine Learning 133