Dataiku

MAY 23, 2023

Once a data science project has progressed through the stages of data cleaning and preparation, analysis and experimentation, modeling, testing, and evaluation, it reaches a critical point.

Data Science 52

Data Science Experimentation Machine Learning Testing 52

KDnuggets

DECEMBER 30, 2019

For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science.

Data Science 99

Data Science Experimentation Optimization IT 99

Dataiku

DECEMBER 28, 2022

It seems like just yesterday that your data science team was a handful of people, actively having to seek out cool use cases for experimentation. As your demand grows, you may consider investing in a data science platform. Now, like many companies, you likely have the opposite problem.

Data Science 52

Data Science Experimentation IT 52

Business Over Broadway

AUGUST 4, 2020

Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals. We know that data professionals, when working on data science and machine learning projects, spend their time on a variety of different activities (e.g.,

Machine Learning 145

Machine Learning Data Science Statistics Experimentation 145

Domino Data Lab

AUGUST 6, 2021

Data science is an incredibly complex field. Framing data science projects within the four steps of the data science lifecycle (DSLC) makes it much easier to manage limited resources and control timelines, while ensuring projects meet or exceed the business requirements they were designed for.

Data Science 75

Data Science Testing Modeling Publishing 75

Cloudera

JANUARY 30, 2019

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. Sound familiar? What is CDSW?

Data Science 72

Data Science Machine Learning Experimentation Cost-Benefit 72

Rocket-Powered Data Science

FEBRUARY 15, 2023

encouraging and rewarding) a culture of experimentation across the organization. These rules are not necessarily “Rocket Science” (despite the name of this blog site), but they are common business sense for most business-disruptive technology implementations in enterprises. Test early and often.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

3AG Systems

DECEMBER 8, 2022

When the Data Scientist role “was relatively new” in 2012, the authors observed that “as more companies attempted to make sense of big data, they realized they needed people who could combine programming, analytics, and experimentation skills.”

Data Science 40

Data Science Experimentation Machine Learning Big Data 40

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

DataRobot Blog

JULY 28, 2022

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. Sensor Data Analysis Examples. The Best Way to Achieve Both Accuracy and Cost Control.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

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.

Experimentation 135

Experimentation Cost-Benefit Data Lake Data Science 135

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. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 362

Marketing Experimentation Metrics Testing 362

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

Data Science and Beyond

SEPTEMBER 19, 2016

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. The hardest parts of data science.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

IBM Big Data Hub

MAY 16, 2019

The next chapter is all about moving from experimentation to true transformation. We are helping businesses activate data as a strategic asset, with desire to maximize the impact of AI as core to the business strategy. Companies are entering “chapter two” of their digital transformation. It’s about gaining speed and scale.

Experimentation 86

Experimentation Data Science Digital Transformation Modeling 86

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 Experimentation Data Science 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 126

Big Data Data Analytics Analytics Predictive Modeling 126

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. These steps also reflect the experimental nature of ML product management.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

DataRobot Blog

NOVEMBER 15, 2022

Organizations are looking to deliver more business value from their AI investments, a hot topic at Big Data & AI World Asia. At the well-attended data science event, a DataRobot customer panel highlighted innovation with AI that challenges the status quo.

Big Data 52

Big Data Experimentation Data-driven Machine Learning 52

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 136

Unstructured Data Data Analytics Analytics Structured Data 136

CIO Business Intelligence

NOVEMBER 21, 2023

Develop citizen data science and self-service capabilities CIOs have embraced citizen data science because data visualization tools and other self-service business intelligence platforms are easy for business people to use and reduce the reporting and querying work IT departments used to support.

IT 136

IT Risk Cost-Benefit Risk Management 136

Cloudera

OCTOBER 4, 2023

Flexible use of compute resources on analytics — which is even more important as we start performing multiple different types of analytics, some critical to daily operations and some more exploratory and experimental in nature, and we don’t want to have resource demands collide.

Data Warehouse 84

Data Warehouse Experimentation Cost-Benefit Dashboards 84

CIO Business Intelligence

FEBRUARY 26, 2024

Empower employees to gain skills in data science, data analytics, ML, and project management. Embedding innovation into an organization often requires a change in mindset — one where experimentation is rewarded, and failed projects are seen as an important part of the learning process.

Experimentation 124

Experimentation Cost-Benefit Digital Transformation Risk 124

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 142

Digital Transformation Unstructured Data Machine Learning Risk Management 142

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 131

Management Digital Transformation Testing Experimentation 131

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 Metrics 101

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 75

Metadata Risk Experimentation Dashboards 75

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

CIO Business Intelligence

MAY 12, 2022

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. A free plan allows experimentation. More focused options are available for particular data sets.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). Algorithm: A set of rules to follow to solve a problem or to decide on a particular action (e.g., They provide more like an FAQ (Frequently Asked Questions) type of an interaction.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

DataRobot Blog

FEBRUARY 28, 2023

A seamless user experience when deploying and monitoring DataRobot models to Snowflake Monitoring service health, drift, and accuracy of DataRobot models in Snowflake “Organizations are looking for mature data science platforms that can scale to the size of their entire business. Learn more about DataRobot hosted notebooks.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

DataRobot Blog

OCTOBER 18, 2022

As AI-driven use cases increase, the number of AI models deployed increases as well, leaving resource-strapped data science teams struggling to monitor and maintain this growing repository. “We These accelerators are specifically designed to help organizations accelerate from data to results. See DataRobot AI Cloud in Action.

Big Data 96

Big Data Experimentation Forecasting Metrics 96

Data Science 101

SEPTEMBER 1, 2023

If I had more room for experimentation though, I’d definitely give svelte and solidjs a try. Honestly, the answer to this question changes every day for me. The short answer would probably be to choose a stack I’m familiar with like React + TypeScript + tailwind + vite.

Experimentation 116

Experimentation Testing Technology IT 116

Domino Data Lab

AUGUST 6, 2021

An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. The complexity of models, and general limitations of expertise with data science among business leaders, creates an environment ripe for risk. Types of Model Risk.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

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.

Metadata 101

Metadata Risk Risk Management Experimentation 101

Domino Data Lab

JANUARY 11, 2021

Arming data science teams with the access and capabilities needed to establish a two-way flow of information is one critical challenge many organizations face when it comes to unlocking value from their modeling efforts. Domino Data Lab and Snowflake: Better Together. About Domino Data Lab. Introduction. About Snowflake.

Recreation/Entertainment 103

Recreation/Entertainment Data Science Data Warehouse Modeling 103