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. Model governance not only reduces risk, it helps to achieve fundamental business goals like production efficiency and profitability.

Machine Learning 145

Machine Learning Modeling Testing Data Science 145

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. RAPIDS brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building. Introduction.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

Rocket-Powered Data Science

JULY 19, 2023

The latter is associated primarily with “watching” the data for interesting patterns, while precursor analytics is associated primarily with training the business systems to quickly identify those specific patterns and events that could be associated with high-risk events, thus requiring timely attention, intervention, and remediation.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

Smart Data Collective

AUGUST 21, 2019

They recognize that the overemphasis on big data has created problems, so they have presented alternatives. Data Science Companies Focus on Optimal Data Utilization Rather than Just Emphasizing Data Scalability. Endor is a leading pioneer in data science. This can lead to a number of problems.

Data Science 70

Data Science Big Data Data Strategy Risk 70

IBM Big Data Hub

MAY 8, 2023

This blog continues the discussion, now investigating the risks of adopting AI and proposes measures for a safe and judicious response to adopting AI. Risk and limitations of AI The risk associated with the adoption of AI in insurance can be separated broadly into two categories—technological and usage.

Insurance 68

Insurance Risk Testing Data Quality 68

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

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. What Is Model Risk? Types of Model Risk.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

Aryng

DECEMBER 12, 2024

As the CEO of a data science consulting company, Ive noticed many organizations fall short in effectively managing their data. Even if their data systems are not technically flawed, they are still unable to solve business problems and drive profitable decisions. A lot of this has to do with their systems.

Risk 52

Risk Consulting Data Science Management 52

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

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 275

Enterprise Modeling Testing Reporting 275

DataKitchen

FEBRUARY 2, 2023

The crazy idea is that data teams are beyond the boom decade of “spending extravagance” and need to focus on doing more with less. This will drive a new consolidated set of tools the data team will leverage to help them govern, manage risk, and increase team productivity. What will exist at the end of 2025?

Metadata 130

Metadata Testing Risk Data Science 130

CIO Business Intelligence

NOVEMBER 20, 2024

“While organizations around the world recognize the value and potential of AI, for AI to be truly effective, it must be tailored to specific industry needs,” said Satish Thomas, corporate VP of business and industry solutions at Microsoft, in a blog post. Among the first models released is E.L.Y.

Manufacturing 122

Manufacturing Marketing Modeling Finance 122

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. While the talk provides both organizational foundations for machine learning as well as product management insights to consider when shipping ML projects, I will be focusing on the latter in this blog post.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

IBM Big Data Hub

OCTOBER 16, 2023

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

Risk 70

Risk Modeling Management Metadata 70

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

The Unofficial Google Data Science Blog

JULY 17, 2018

In this blog post we describe one of these instances — Google search deciding when to check if web pages have changed. Through this example, we discuss some of the special considerations impacting a data scientist when designing solutions to improve decision-making deep within software infrastructure.

Data Science 85

Data Science Snapshot Data Processing Optimization 85

DataRobot Blog

FEBRUARY 21, 2022

With growing pressure on data scientists, every organization needs to ensure that their teams are empowered with the right tools. Data science notebooks have become a crucial part of the data science practice. Governance and Security: How can I ensure security of my data? How can I mitigate security risks?

Data Science 52

Data Science Recreation/Entertainment Risk Visualization 52

bridgei2i

NOVEMBER 25, 2020

Supply Chain Data Science in many organizations has completely transformed the market as it brings in profitability and increases sales figures. Benefits of Data Science in Supply Chain Management. Thus, the benefits of AI in data science are quite clear.

Data Science 52

Data Science Cost-Benefit Sales Predictive Analytics 52

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

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

Cloudera

JULY 30, 2021

I have many large and important customers that heavily rely on our technology to improve customer experience, calculate customer risk profiles in real-time or address key regulatory requirements.” The post #ClouderaLife Spotlight: Vinicius Cardoso, Sr Solutions Engineering appeared first on Cloudera Blog. Vinicius Cardoso, Sr.

Sales 122

Sales Big Data Data-driven Risk 122

CIO Business Intelligence

APRIL 15, 2022

But according to the UK’s Turing Institute, a national center for data science and AI, the predictive tools made little to no difference. The patients who were lying down were much more likely to be seriously ill, so the algorithm learned to identify COVID risk based on the position of the person in the scan.

Analytics 145

Analytics Machine Learning Insurance Testing 145

Cloudera

APRIL 23, 2024

The Hackathon was intended to provide data science experts with access to Cloudera machine learning to develop their own Accelerated Machine Learning Project (AMP) focused on solving one of the many environmental challenges facing the world today. appeared first on Cloudera Blog.

Machine Learning 91

Machine Learning Data Science Data-driven Data Processing 91

Cloudera

SEPTEMBER 28, 2020

The data lifecycle model ingests data using Kafka, enriches that data with Spark-based batch process, performs deep data analytics using Hive and Impala, and finally uses that data for data science using Cloudera Data Science Workbench to get deep insights. Phase 1: Planning.

Testing 132

Testing Metadata Risk Data Science 132

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

IBM Big Data Hub

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

CIO Business Intelligence

JULY 23, 2024

Pressure from above Some companies may overstate their AI use because they don’t understand what AI encompasses, says Shargel, who co-authored a blog post in January about AI washing. This is a new a new technology, working with new data, going after new use cases, and that’s challenging,” he says.

Uncertainty 129

Uncertainty Machine Learning Business Intelligence Marketing 129

O'Reilly on Data

JANUARY 22, 2019

In this blog post, we explore three types of errors inherent in all financial models, with a simple example of a model in TensorFlow Probability (TFP). They trade the markets using quantitative models based on non-financial theories such as information theory, data science, and machine learning. Finance is not physics.

Modeling 135

Modeling Finance Uncertainty Data mining 135

IBM Big Data Hub

OCTOBER 4, 2022

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. This is due to: An inability to access the right data. Manual processes that introduce risk and make it hard to scale.

Metadata 97

Metadata Risk Management Risk Experimentation 97

bridgei2i

APRIL 23, 2020

The banking sector globally is definitely going to see impact, some more grave than the others and most of them are announcing short to mid term measure both from a customer and business risk mitigation standpoint. Europe is in worse shape than America, with banks in UK, Italy and Germany in the risk of being in red.

Risk 52

Risk Management Scorecard Forecasting 52

Smart Data Collective

APRIL 10, 2020

Towards Data Science published a blog post discussing similar research on the topic. Most accounting systems track risk assessments. Accounting Today has provided a list of ways that machine learning is changing the accounting industry. You can account for this by integrating these trends into your accounting system.

Machine Learning 119

Machine Learning Cost-Benefit Big Data Forecasting 119

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. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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 Data Warehouse 41

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 Finance 52

Domino Data Lab

JANUARY 21, 2021

This blog explores the challenges associated with doing such work manually, discusses the benefits of using Pandas Profiling software to automate and standardize the process, and touches on the limitations of such tools in their ability to completely subsume the core tasks required of data science professionals and statistical researchers.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

datapine

APRIL 14, 2022

After all, these are some pretty massive industries with many examples of big data analytics, and the rise of business intelligence software is answering what data management needs. However, the usage of data analytics isn’t limited to only these fields. Download our free summary outlining the best big data examples!

Big Data 244

Big Data Recreation/Entertainment Analytics Data-driven 244