DataRobot Blog

JULY 28, 2022

Of course, finding a compromise is necessary to a certain degree, but rather than simply compromising, finding the optimal solution within that trade-off is the key to creating maximum business value. While there are many differences between Data Science Competitions and business, there are also similarities.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Don’t expect agreement to come simply. Lack of clarity about metrics is technical debt worth paying down.

Marketing 363

Marketing Experimentation Metrics Testing 363

DataRobot Blog

MARCH 8, 2023

As a result, enterprises can now get powerful insights and predictive analytics from their business data by integrating DataRobot-trained machine learning models into their SAP-specific business processes and applications, while bringing data science and analytics teams and business users closer together for better outcomes.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

CIO Business Intelligence

MAY 21, 2024

Along with code-generating copilots and text-to-image generators, which leverage a combination of LLMs and diffusion processing, LLMs are at the core of most generative AI experimentation in business today. We’re looking to [help our customers] schedule people optimally with the right skill at the right time,” he says.

IT 121

IT Cost-Benefit Experimentation Forecasting 121

CIO Business Intelligence

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. Are data science teams set up for success? Have business leaders defined realistic success criteria and areas of low-risk experimentation?

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

CIO Business Intelligence

JUNE 14, 2023

Certification of Professional Achievement in Data Sciences The Certification of Professional Achievement in Data Sciences is a nondegree program intended to develop facility with foundational data science skills. How to prepare: No prior computer science or programming knowledge is necessary.

Big Data 124

Big Data Data Analytics Analytics Predictive Modeling 124

Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. Over the last couple years, it would be hard to blame anyone for being overwhelmed looking at the data science platform market landscape. Proprietary (often GUI-driven) data science platforms.

Data Science 65

Data Science Marketing Data-driven Statistics 65

Rocket-Powered Data Science

OCTOBER 16, 2020

Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more. The data age has been marked by numerous “hype cycles.” 4) What data do you have to fuel the algorithms, the training and the modeling processes? (5) The Age of Hype Cycles.

Digital Transformation 142

Digital Transformation Experimentation Data Strategy Data Science 142

Cloudera

OCTOBER 4, 2023

In telecommunications, fast-moving data is essential when we’re looking to optimize the network, improving quality, user satisfaction, and overall efficiency. In financial services, fast-moving data is critical for real-time risk and threat assessments. Ready to stop blinking and never miss a beat?

Data Warehouse 89

Data Warehouse Experimentation Cost-Benefit Dashboards 89

Domino Data Lab

MARCH 20, 2019

Models are so different from software — e.g., they require much more data during development, they involve a more experimental research process, and they behave non-deterministically — that organizations need new products and processes to enable data science teams to develop, deploy and manage them at scale.

Management 97

Management Experimentation Data Science Modeling 97

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. The goal of Alteryx’s Analytic Process Automation (APA) platform is to help you build a pipeline that cleans data before applying the best data science and machine learning algorithms.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

Domino Data Lab

SEPTEMBER 10, 2020

Yet despite all this hard work, few models ever make it into production (VentureBeat AI concluded that just 13% of data science projects make it into production) and in terms of delivering value to the business, Gartner predicts that only 20% of analytics projects will deliver business outcomes that improve performance.

Modeling 101

Modeling IT Data Science Experimentation 101

AWS Big Data

JUNE 20, 2023

Data scientists can reduce the time spent preparing and copying datasets, and instead focus on data feature engineering, experimentation, and analyzing data at scale. Solution overview Data scientists are generally accustomed to working with large datasets.

Data Lake 72

Data Lake Data Science Recreation/Entertainment Experimentation 72

CIO Business Intelligence

MARCH 21, 2022

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist salary. Data scientist skills.

Unstructured Data 130

Unstructured Data Data Analytics Analytics Structured Data 130

CIO Business Intelligence

OCTOBER 1, 2022

The digital transformation of P&G’s manufacturing platform will enable the company to check product quality in real-time directly on the production line, maximize the resiliency of equipment while avoiding waste, and optimize the use of energy and water in manufacturing plants. Data and AI as digital fundamentals.

Manufacturing 143

Manufacturing Digital Transformation IoT Internet of Things 143

CIO Business Intelligence

DECEMBER 19, 2023

In the 2023 State of Data Science and Machine Learning Report , only 18% of respondents said that at least half their machine learning models make it into production. If CIOs don’t improve conversions from pilot to production, they may find their investors losing patience in the process and culture of experimentation.

Digital Transformation 138

Digital Transformation Unstructured Data Machine Learning Risk Management 138

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 95

Experimentation Statistics Testing Strategy 95

IBM Big Data Hub

MAY 9, 2023

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation to become business critical for many organizations. It drives an AI governance solution without the excessive costs of switching from your current data science platform.

Metadata 72

Metadata Risk Experimentation Dashboards 72

IBM Big Data Hub

OCTOBER 4, 2022

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. Today, AI presents an enormous opportunity to turn data into insights and actions, to amplify human capabilities, decrease risk and increase ROI by achieving break through innovations. Platforms and practices not optimized for AI.

Metadata 97

Metadata Risk Risk Management Experimentation 97

DataRobot

JANUARY 13, 2022

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. Most data scientists are strong in one or two of these areas, but not all three. This frees up data scientists to focus on more complex analytical tasks.

ROI 52

ROI Machine Learning Experimentation Data Science 52

Domino Data Lab

OCTOBER 14, 2020

Experimentation and collaboration are built into the core of the platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Our scientists and teams are familiar with working in Spark on EMR and using data with our existing feature store and data warehouse.

Experimentation 99

Experimentation Deep Learning Modeling Testing 99

Cloudera

APRIL 9, 2021

To effectively leverage their predictive capabilities and maximize time-to-value these companies need an ML infrastructure that allows them to quickly move models from data pipelines, to experimentation and into the business. Finally, CML has built-in model security and governance thanks to Cloudera Shared Data Experience (SDX).

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

AWS Big Data

NOVEMBER 2, 2023

Solution overview GoDaddy’s intelligent compute platform envisions simplification of compute operations for all personas, without limiting power users, to ensure out-of-box cost and performance optimization for data and ML workloads. He has over 6 years of experience working in the field of big data and data science.

Cost-Benefit 88

Cost-Benefit Big Data Testing Optimization 88

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. The approach they’ve used applies to other popular data science APIs such as NumPy , Tensorflow , and so on.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. But these are not usually amenable to A/B experimentation.

Experimentation 68

Experimentation Sales Metrics Measurement 68

DataRobot Blog

JANUARY 10, 2023

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. 42% of data scientists are solo practitioners or on teams of five or fewer people.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Domino Data Lab

FEBRUARY 12, 2020

This post is for people making technology decisions, by which I mean data science team leads, architects, dev team leads, even managers who are involved in strategic decisions about the technology used in their organizations. If you are interested in additional insights, register for the upcoming Ray Summit. Introduction. asyncio ?,

Experimentation 137

Experimentation Modeling Data Science Management 137

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. AGI wouldn’t just perceive its surroundings; it would understand them.

Cost-Benefit 89

Cost-Benefit Modeling Manufacturing Interactive 89

FineReport

DECEMBER 11, 2019

In the past few years, the term “data science” has been widely used, and people seem to see it in every field. Big Data”, “Business Intelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis. By Michael Milton.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

This is essentially the same as finding a truly useful objective to optimize. accounting for effects "orthogonal" to the randomization used in experimentation. The first thing you’ll want to do is to run your test for a long time with fixed experimental units, in our case cookies.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. back to the structure of the dataset. Let’s look through some antidotes. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Insight

MARCH 27, 2019

DeepAugment is an AutoML tool focusing on data augmentation. It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. To address this problem, Google published AutoAugment last year, which discovers optimized augmentations for the given dataset using reinforcement learning.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

Ontotext

NOVEMBER 2, 2023

This is partly because integrating and moving data is not the only problem. The data itself is stored in a way that is not optimal for extracting insight. Unlocking additional value from data requires context, relationships, and structure, none of which are present in the way most organizations store their data today.

IT 69

IT Cost-Benefit Data-driven Technology 69

Insight

APRIL 28, 2020

Originally posted on Open Data Science (ODSC). In this article, we share some data-driven advice on how to get started on the right foot with an effective and appropriate screening process. Designing a Data Science Interview Onsite interviews are indispensable, but they are time-consuming.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

FineReport

DECEMBER 19, 2019

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. Data Analysis Libraries.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98