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 312

Testing Machine Learning Consulting Data Quality 312

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

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 299

Advertising Cost-Benefit ROI Machine Learning 299

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

CIO Business Intelligence

NOVEMBER 27, 2024

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

Data Architecture 128

Data Architecture Cost-Benefit Machine Learning Experimentation 128

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

CIO Business Intelligence

DECEMBER 4, 2024

At AWS re:Invent Tuesday, JPMorgan Chase Global CIO Lori Beer detailed the evolving partnership between the financial services giant and AWS, which finds JPMorgan pushing the AWS SageMaker machine learning platform and AWS Bedrock generative AI platform beyond experimentation into production applications.

Experimentation 122

Experimentation Risk Marketing Enterprise 122

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

CIO Business Intelligence

NOVEMBER 19, 2024

This stark contrast between experimentation and execution underscores the difficulties in harnessing AI’s transformative power. Data privacy and compliance issues Failing: Mismanagement of internal data with external models can lead to privacy breaches and non-compliance with regulations. Of those, just three are considered successful.

Consulting 137

Consulting Cost-Benefit Experimentation Optimization 137

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

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 267

IT Modeling Testing Risk 267

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 Experimentation 126

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

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 95

Data Warehouse Optimization Analytics Sales 95

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

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 90

Machine Learning Modeling Metadata Recreation/Entertainment 90

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 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

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

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

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Let's listen in as Alistair discusses the lean analytics model… The Lean Analytics Cycle is a simple, four-step process that shows you how to improve a part of your business. But it is not routine.

Metrics 157

Metrics KPI Analytics Key Performance Indicator 157

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. Optimize later. Step 4: Iterate quickly.

Machine Learning 98

Machine Learning Enterprise Experimentation Modeling 98

DataRobot

MARCH 10, 2021

Set the goal to be achieved or optimized. Deploy the machine learning model into production. The experimenters simulated experiences in online travel and online dating, varying the time people waited for a search result. Fit pattern-matching algorithms. Humans and AI Best Practices.

Experimentation 145

Experimentation Machine Learning Key Performance Indicator Reporting 145

Domino Data Lab

MARCH 20, 2019

Our mission at Domino is to enable organizations to put models at the heart of their business. Today we’re announcing two major new capabilities in Domino that make model development easier and faster for data scientists. This pain point is magnified in organizations with teams of data scientists working on numerous experiments.

Management 97

Management Experimentation Data Science Modeling 97

CIO Business Intelligence

APRIL 24, 2024

But to find ways it can help grow a company’s bottom line, CIOs have to do more to understand a company’s business model and identify opportunities where gen AI can change the playing field. We’re mostly still optimizing our sales and marketing processes with CRM tools,” he says. And hyper-personalization at scale.”

Sales 128

Sales Finance Experimentation Cost-Benefit 128

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