Smart Data Collective

MARCH 13, 2020

It doesn’t matter what the project or desired outcome is, better data science workflows produce superior results. 5 Tips for Better Data Science Workflows. Data science is a complex field that requires experience, skill, patience, and systematic decision-making in order to be successful.

Data Science 101

Data Science Key Performance Indicator Knowledge Discovery Visualization 101

O'Reilly on Data

FEBRUARY 26, 2021

We then added Justin Norman, VP of Data Science at Yelp, to the team. These articles show you how to minimize your risk at every stage of the project, from initial planning through to post-deployment monitoring and testing. We decided that would be a good topic for an article, and possibly more.

Management 274

Management Testing Data Science Risk 274

Domino Data Lab

JULY 28, 2021

For companies investing in data science, the stakes have never been so high. According to a recent survey from New Vantage Partners (NVP), 62 percent of firms have invested over $50 million in big data and AI, with 17 percent investing more than $500 million. The Challenges of Scaling Data Science.

Data Science 59

Data Science Enterprise Cost-Benefit Data-driven 59

Rocket-Powered Data Science

FEBRUARY 15, 2023

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Here are my 10 rules ( i.e., Business Strategies for Deploying Disruptive Data-Intensive, AI, and ChatGPT Implementations): Honor business value above all other goals. Test early and often.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

Domino Data Lab

FEBRUARY 20, 2019

Eugene Mandel , Head of Product at Superconductive Health , recently dropped by Domino HQ to candidly discuss cross-team collaboration within data science. Eugene Mandel , Head of Product at Superconductive Health , recently dropped by Domino HQ to discuss cross-team collaboration within data science.

Data Science 76

Data Science Testing Software Uncertainty 76

O'Reilly on Data

NOVEMBER 28, 2023

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. We’re not encouraging skepticism or fear, but companies should start AI products with a clear understanding of the risks, especially those risks that are specific to AI.

Enterprise 267

Enterprise Modeling Testing Reporting 267

Domino Data Lab

AUGUST 6, 2021

All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards. The primary focus of model governance involves tracking, testing and auditing. First is the data the model is using.

Machine Learning 145

Machine Learning Modeling Testing Risk 145

Jet Global

SEPTEMBER 1, 2023

But we’re also seeing its use expand in other industries, like Financial Services applications for credit risk assessment or Human Resources applications to identify employee trends. Analysts can use predictive analytics to foresee if a change will help them reduce risks, improve operations, and/or increase revenue.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52

CIO Business Intelligence

MARCH 14, 2024

Adopting and scaling AI Some of the NAO’s advice to government could also apply to enterprises, especially those with a similar aversion to risk and change. Testing and piloting AI is key to successful adoption, the study found, and the government bodies surveyed are doing well on this score, with 70% already doing so.

Testing 107

Testing Risk Digital Transformation Statistics 107

DataRobot Blog

JUNE 3, 2022

To understand the capabilities and address the risks of AI, all of us – developers, policy-makers, end-users, bystanders – must have a shared understanding of what AI is, how it can be applied to the benefit of humanity, and the risks involved when implementing it without guardrails in place to mitigate bias and harm. Download now.

Risk 52

Risk Predictive Modeling Modeling Technology 52

Alation

JULY 28, 2021

As data science processes continue to become operationalized and embedded within business processes, the importance of governing those processes continues to rise. While governance has been a major focus for many years when it comes to managing data, governance focused on data science processes is still far less mature.

Data Science 52

Data Science Data Governance Modeling Software 52

Rocket-Powered Data Science

JULY 14, 2023

As I progressed in my career into management roles for enterprise data systems, I gained a deeper understanding and appreciation of the synergies and interdependencies between system and user requirements. Analytics products represent the user-facing and client-facing derived value from an organization’s data stores.

Analytics 166

Analytics Internet of Things Prescriptive Analytics Data Science 166

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. I held out 20% of this as a test set and used the remainder for training and validation.

Risk 67

Risk ROI Modeling Metrics 67

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Data science. Session Summary.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Insight

MARCH 8, 2019

Insight is committed to helping more women launch careers in tech , so in celebration of International Women’s Day 2019, we wanted to share a few of the projects completed by women participating in the January 2019 Data Science and Health Data Science sessions. A team of four Fellows from our Silicon Valley programs?— ?Tayler

Data Science 75

Data Science Sales Machine Learning Reporting 75

DataRobot

OCTOBER 5, 2021

But the technology’s ability to unleash rapid impact with great scope and unique dimensions can also increase organizational risk. For companies implementing AI systems, that risk extends beyond revenue to the reputational damage of using an algorithm that is perceived to be discriminatory or harmful to vulnerable groups.

Risk Management 95

Risk Management Testing Risk Data Science 95

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. For example, we may prefer one model to generate a range, but use a second scenario-based model to “stress test” the range. Our team does a lot of forecasting.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Decision Management Solutions

MARCH 25, 2019

Nancy Casbarro and Deb Zawisa of Novarico recently published a new paper on Data Science in Insurance: Expansion and Key Issues subscription required) that was summarized in this nice little article on Dig-in 3 challenges facing insurers in data science implementation. 2 – Attracting talent.

Insurance 75

Insurance Data Science Testing Modeling 75

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. CIOs should look for other operational and risk management practices to complement transformation programs.

Digital Transformation 142

Digital Transformation Unstructured Data Machine Learning Risk Management 142

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

DataKitchen

JANUARY 5, 2021

Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance. Each of the roles mentioned above views the world through a preferred set of tools: Data Center/IT – Servers, storage, software.

Testing 147

Testing Data Governance Metadata Visualization 147

bridgei2i

NOVEMBER 20, 2020

Episode 2: AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower. AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower. Today the Chief Risk Officers(CROs) struggle with the critical task of monitoring and assessing key risks in real time and firefight to mitigate any critical issues that arise.

Risk Management 92

Risk Management Risk Management Metrics 92

CIO Business Intelligence

MAY 15, 2024

“If you have a data center that happens to have capacity, why pay someone else?” According to Synopsys’ open source security and risk analysis released in February, 96% of all commercial code bases contained open source components. It also focuses largely on risk and governance issues.

Modeling 135

Modeling Risk Software Enterprise 135

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. Devops teams now look to shift left security and implement continuous testing to develop more innovative, secure, and reliable features from the start.

Digital Transformation 145

Digital Transformation Data Governance Experimentation Machine Learning 145

IBM Big Data Hub

FEBRUARY 22, 2024

Defense leaders focused on operationalizing the responsible curation of AI must first agree upon a shared vocabulary—a common culture that guides safe, responsible use of AI—before they implement technological solutions and guardrails that mitigate risk. Model output is based on the data it was trained on.

Metadata 96

Metadata Measurement Risk Modeling 96

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 83

Testing Experimentation Data Quality Machine Learning 83

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

DataKitchen

FEBRUARY 21, 2023

are more efficient in prioritizing data delivery demands.” Release New Data Engineering Work Often With Low Risk: “Testing and release processes are heavily manual tasks… automate these processes.” His team is already on Azure and uses ADF, Synapse, PowerBi, and several powerful Azure Data Science tools.

Data-driven 130

Data-driven Testing Risk Cost-Benefit 130

Cloudera

MAY 19, 2021

This year, we expanded our partnership with NVIDIA , enabling your data teams to dramatically speed up compute processes for data engineering and data science workloads with no code changes using RAPIDS AI. In this tutorial, we will illustrate how RAPIDS can be used to tackle the Kaggle Home Credit Default Risk challenge.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

datapine

OCTOBER 21, 2020

For our first example of big data in healthcare, we will look at one classic problem that any shift manager faces: how many people do I put on staff at any given time period? If you put on too many workers, you run the risk of having unnecessary labor costs add up. It’s the most widespread application of big data in medicine.

Big Data 364

Big Data Data Analytics Analytics Cost-Benefit 364

O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. 4 But is it too broad to be useful?

Risk 357

Risk Modeling Data Processing Software 357

CIO Business Intelligence

AUGUST 24, 2023

Gen AI has the potential to magnify existing risks around data privacy laws that govern how sensitive data is collected, used, shared, and stored. We’re getting bombarded with questions and inquiries from clients and potential clients about the risks of AI.” The risk is too high.”

Uncertainty 98

Uncertainty Risk Testing Enterprise 98

KDnuggets

MARCH 17, 2022

Check out this article on using CTGANs to create synthetic datasets for reducing privacy risks, training and testing machine learning models, and developing data-centric AI products.

Machine Learning 137

Machine Learning Testing Risk Modeling 137

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources.

Marketing 361

Marketing Experimentation Metrics Testing 361

Cloudera

SEPTEMBER 28, 2020

Cloudera has found that customers have spent many years investing in their big data assets and want to continue to build on that investment by moving towards a more modern architecture that helps leverage the multiple form factors. Data Science and machine learning workloads using CDSW. Test and QA. Test and QA.

Testing 131

Testing Metadata Risk Data Science 131

Rocket-Powered Data Science

JUNE 17, 2022

Here is a list of my top moments, learnings, and musings from this year’s Splunk.conf : Observability for Unified Security with AI (Artificial Intelligence) and Machine Learning on the Splunk platform empowers enterprises to operationalize data for use-case-specific functionality across shared datasets. is here, now!

Machine Learning 238

Machine Learning Recreation/Entertainment Risk Business Objectives 238

Cloudera

MAY 22, 2018

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. This leads to wasted time and effort during research and collaboration or, worse, compliance risk. Cloudera Data Science Workbench 1.4.x

Data Science 41

Data Science Snapshot Machine Learning Metadata 41