Domino Data Lab

JUNE 10, 2020

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. Domino’s best-in-class Workbench is now even more powerful for data scientists.

Data Science 82

Data Science Enterprise Experimentation Modeling 82

CIO Business Intelligence

NOVEMBER 21, 2023

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT. There may be times when department-specific data needs and tools are required.

IT 131

IT Risk Cost-Benefit Risk Management 131

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 127

Machine Learning Data Science Experimentation IoT 127

Domino Data Lab

JULY 28, 2021

The data science lifecycle (DLSC) has been defined as an iterative process that leads from problem formulation to exploration, algorithmic analysis and data cleaning to obtaining a verifiable solution that can be used for decision making. The data science process in a business environment begins with the Manage stage.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

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 140

Digital Transformation Unstructured Data Machine Learning Risk Management 140

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

MARCH 7, 2023

Some IT organizations elected to lift and shift apps to the cloud and get out of the data center faster, hoping that a second phase of funding for modernization would come. Then, often reporting to risk, compliance, or security organizations, are separate data governance teams focused on data security, privacy, and quality.

Digital Transformation 144

Digital Transformation Data Governance Experimentation Machine Learning 144

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. Closing the Value Gap: Reducing AI Cycle Time.

Big Data 52

Big Data Experimentation Data-driven Machine Learning 52

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

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. Challenges around managing risk and reputation Customers, employees and shareholders expect organizations to use AI responsibly, and government entities are starting to demand it.

Metadata 72

Metadata Risk Experimentation Dashboards 72

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 116

Experimentation Cost-Benefit Digital Transformation Risk 116

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. Challenges around managing risk.

Metadata 98

Metadata Risk Risk Management Experimentation 98

CIO Business Intelligence

AUGUST 1, 2023

As many CIOs prepare their 2024 budgets and digital transformation priorities, developing a strategy that seeks opportunities to evolve business models, targets near-term operational impacts, prioritizes where employees should experiment, and defines AI-related risk-mitigating plans is imperative.

Digital Transformation 98

Digital Transformation Unstructured Data Strategy Experimentation 98

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. An option that can sometimes work is to use only the data from users assigned in the final epoch.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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.

Data Warehouse 87

Data Warehouse Experimentation Cost-Benefit Dashboards 87

CIO Business Intelligence

APRIL 15, 2024

Proceeding with caution While H&R Block’s leadership and board were enticed by the possibilities of gen AI, Lowden notes he had to address some concerns before they fully bought into the project, especially with regard to safety and data privacy. “No No one wants to implement AI that has a high risk of bias,” he says. “We

Testing 77

Testing Experimentation Data Quality Machine Learning 77

DataRobot

JANUARY 20, 2022

As health and care delivery converges, analytical staff will be required to work across more boundaries with larger volumes of data than ever before. . Specialized teams from DataRobot and Snowflake will enable ICSs to mitigate data governance and model bias risk with confidence. Public sector data sharing.

Data-driven 145

Data-driven Experimentation Modeling Predictive Modeling 145

DataRobot Blog

MARCH 7, 2023

DataRobot on Azure accelerates the machine learning lifecycle with advanced capabilities for rapid experimentation across new data sources and multiple problem types. DataRobot’s vision is to bring together all data types, all users, and all environments to deliver critical business insights for every organization.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 307

Enterprise Deep Learning Data Governance Risk 307

CIO Business Intelligence

SEPTEMBER 27, 2023

By suggesting a number of use cases, we further encourage experimentation and creative application.” Dayforce Labs is complemented by our data science team, as there is no AI without data,” Lloyd says. This includes carefully examining how data can be used to create better outcomes through AI.”

Strategy 122

Strategy Risk Data-driven Modeling 122

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

IBM Big Data Hub

OCTOBER 20, 2023

AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.

Machine Learning 73

Machine Learning Manufacturing Cost-Benefit Deep Learning 73

IBM Big Data Hub

JANUARY 17, 2023

You can select a hybrid integration strategy that aligns with your organization’s business strategy to meet the needs of your data consumers wanting to access and utilize the data. Data science and MLOps. AI is no longer experimental. Start a trial. Artificial intelligence (AI) is no longer a choice.

Data-driven 56

Data-driven Enterprise Data Governance Prescriptive Analytics 56

CIO Business Intelligence

JULY 6, 2022

Our strategy is essentially to leverage the public cloud’s economies of scale, to drive business value, to lower risk, to increase resiliency, and really to ensure our infrastructure is evergreen. So, for instance, let’s say I have an idea or one of our leading data scientists has an idea for a next-generation fraud model.

Experimentation 87

Experimentation Testing Strategy Risk 87

CIO Business Intelligence

OCTOBER 1, 2022

“Accessing this level of data, at scale, is rare within the consumer goods industry,” Cretella says. Data and AI as digital fundamentals. It has moved past what Cretella calls the “experimentation phase” with scaled solutions and increasingly sophisticated AI applications.

Manufacturing 144

Manufacturing Digital Transformation IoT Internet of Things 144

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

Data Science 101

OCTOBER 15, 2023

Others like Rigetti and IonQ went public early (the former risking delisting and the latter a huge SPAC). That’s going to use the various existing computing technology in and around the experimentation of this new capability. That’s a risk. QPUs, GPUs, CPUs, you name it. What can you say about the book you are writing?

Risk 107

Risk Marketing Consulting Technology 107

Sisense

JANUARY 21, 2020

In general, it is not possible to give a rule of thumb about when data should be partitioned or combined. A new drug promising to reduce the risk of heart attack was tested with two groups. When the data is combined, it seems that the drug reduces the risk of getting a heart attack. It really depends on the circumstances.

Testing 104

Testing Risk Data-driven Statistics 104

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41

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. It analyzes historical data and news articles, confirming a possible market correction.

Cost-Benefit 91

Cost-Benefit Modeling Manufacturing Interactive 91

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.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

CIO Business Intelligence

OCTOBER 4, 2023

With a little experimentation, it’s cheap and easy to find applications that can provide business benefits, creating a false perception of value. He says companies should prioritize potential AI use cases first by impact, second by risk, and third by data: “Do we have the data to do this use case? Is it accessible?

Cost-Benefit 136

Cost-Benefit Manufacturing Machine Learning Experimentation 136

Ontotext

NOVEMBER 2, 2023

This shift from relational to graph approach has been well-documented by Gartner who advise that “using graph techniques at scale will form the foundation of modern data and analytics” and “graph technologies will be used in 80% of data and analytics innovations by 2025.”

IT 69

IT Cost-Benefit Data-driven Technology 69

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

IBM Big Data Hub

SEPTEMBER 25, 2023

Because much of the work done on their data lake is exploratory in nature, many users want to execute untested queries on petabytes of data. Large, untested workloads run the risk of hogging all the resources. Uber chose Presto for the flexibility it provides with compute separated from data storage.

OLAP 88

OLAP Data Lake Data-driven Snapshot 88

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 ). We assume that you need to build production-grade software.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110