Cloudera

APRIL 9, 2021

The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Data Collection – streaming data.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Rocket-Powered Data Science

FEBRUARY 15, 2023

encouraging and rewarding) a culture of experimentation across the organization. there can be objective assessments of failure, lessons learned, and subsequent improvements), then friction can be minimized, failure can be alleviated, and innovation can flourish. Test early and often. Expect continuous improvement.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

DataRobot Blog

MARCH 8, 2023

Today, SAP and DataRobot announced a joint partnership to enable customers connect core SAP software, containing mission-critical business data, with the advanced Machine Learning capabilities of DataRobot to make more intelligent business predictions with advanced analytics.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

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. Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

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? Are they working on problems that can yield meaningful business outcomes?

Machine Learning 127

Machine Learning Data Science Experimentation IoT 127

DataRobot Blog

JULY 28, 2022

Rapid advances in machine learning in recent years have begun to lower the technical hurdles to implementing AI, and various companies have begun to actively use machine learning. As a DataRobot data scientist , I have worked with team members on a variety of projects to improve the business value of our customers.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

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 125

Big Data Data Analytics Analytics Predictive Modeling 125

Domino Data Lab

AUGUST 6, 2021

Data science is an incredibly complex field. When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Let’s use an example business problem to illustrate the four steps of the data science lifecycle.

Data Science 75

Data Science Testing Modeling Publishing 75

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

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Without clarity in metrics, it’s impossible to do meaningful experimentation. Experimentation should show you how your customers use your site, and whether a recommendation engine would help the business.

Marketing 362

Marketing Experimentation Metrics Testing 362

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

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

CIO Business Intelligence

DECEMBER 19, 2023

This year’s technology darling and other machine learning investments have already impacted digital transformation strategies in 2023 , and boards will expect CIOs to update their AI transformation strategies frequently. Luckily, many are expanding budgets to do so. “94%

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning. (4)

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

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

MAY 12, 2022

The tools include sophisticated pipelines for gathering data from across the enterprise, add layers of statistical analysis and machine learning to make projections about the future, and distill these insights into useful summaries so that business users can act on them. On premises or in SAP cloud. Per user, per month.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

CIO Business Intelligence

MARCH 21, 2022

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. Learn from data scientists about their responsibilities and find out how to launch a data science career. |

Unstructured Data 136

Unstructured Data Data Analytics Analytics Structured Data 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 83

Data Warehouse Experimentation Cost-Benefit Dashboards 83

O'Reilly on Data

MARCH 19, 2020

We use it as a data source for our annual platform analysis , and we’re using it as the basis for this report, where we take a close look at the most-used and most-searched topics in machine learning (ML) and artificial intelligence (AI) on O’Reilly [1]. What’s driving this growth? disproportionately involve Python.

Deep Learning 276

Deep Learning Machine Learning Data-driven Modeling 276

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Also, loyalty leaders infuse analytics into CX programs, including machine learning, data science and data integration.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. 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. Monitoring with Machine Learning.

Big Data 96

Big Data Forecasting Experimentation Metrics 96

Domino Data Lab

NOVEMBER 14, 2019

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. James Murdoch, Chandan Singh, Karl Kumber, and Reza Abbasi-Asi’s recent paper, “Definitions, methods, and applications in interpretable machine learning” Introduction.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

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

The Unofficial Google Data Science Blog

APRIL 23, 2024

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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

DataRobot

JUNE 17, 2021

Searching online, I found there are 1,330 publications that mentioned Eureqa in their analysis , and 1,637 citations of the principal publication that inspired Eureqa in Science. This search for mathematical formulas makes Eureqa different from other machine learning algorithms. So What is Eureqa? Eureqa Enhancements in 7.1

Modeling 52

Modeling Experimentation Machine Learning Measurement 52

Cloudera

AUGUST 20, 2020

Businesses had to literally switch operations, and enable better collaboration and access to data in an instant — while streamlining processes to accommodate a whole new way of doing things. But out of disruption, we’ve seen incredible innovation born into the enterprise. But UOB didn’t stop there. That’s really important.

Enterprise 120

Enterprise Experimentation Uncertainty Deep Learning 120

Smart Data Collective

MAY 20, 2019

Machine Learning Experience is a Must. By 2020, over 40 percent of all data science tasks will be automated. Machine learning technology and its growing capability is a huge driver of that automation.

Analytics 111

Analytics Machine Learning Business Intelligence Big Data 111

CIO Business Intelligence

OCTOBER 1, 2022

P&G is also piloting the use of IIoT, advanced algorithms, machine learning (ML), and predictive analytics to improve manufacturing efficiencies in the production of paper towels. Accessing this level of data, at scale, is rare within the consumer goods industry,” Cretella says. Data and AI as digital fundamentals.

Manufacturing 145

Manufacturing Digital Transformation IoT Internet of Things 145

DataRobot

JUNE 23, 2021

As they strive to improve models, data scientists continually try new approaches to refine their predictions. To help data scientists experiment faster, DataRobot has added Composable ML to automated machine learning. In data science , the best results come through experimentation. Run AutoPilot.

Machine Learning 52

Machine Learning Modeling Experimentation Visualization 52

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. 42% of data scientists are solo practitioners or on teams of five or fewer people. 42% of data scientists are solo practitioners or on teams of five or fewer people. Auto-scale compute.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

DataRobot

APRIL 19, 2021

Most modern artificial intelligence systems are powered by machine learning algorithms , which learn by example. Data scientists train the algorithms using datasets that contain curated learning examples. They need data assembly, data science, integration, and visualization skills.

Machine Learning 52

Machine Learning Visualization Data-driven Data Science 52

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. He also encouraged data scientists to understand how new data science algorithms rearrange power as well as how the history of data is a story of truth and power.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

Domino Data Lab

JUNE 13, 2019

2018 , 2019 ], the rediscovery of the 50,000 lost MNIST test digits provides an opportunity to quantify the degradation of the official MNIST test set over a quarter-century of experimental research.” . “In the same spirit as [Recht et al., ” They also were able to.

Testing 83

Testing Data Science Experimentation Metadata 83

Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ).

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110