CIO Business Intelligence

APRIL 4, 2022

If a specific email or login credential is identified in a potentially fraudulent transaction, all future interactions with this email or credential can be subject to additional scrutiny. Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI).

Technology 52

Technology Deep Learning Cost-Benefit Data Quality 52

Sisense

APRIL 15, 2021

From data preparation , with attendant data quality assessment, to connecting to datasets and performing the analysis itself, helpful AI elements, invisibly integrated into the platform, make analysis smoother and more intuitive. Call centers already report that AI chatbots receive higher customer ratings than human agents!

Analytics 98

Analytics Data Science Modeling Machine Learning 98

Cloudera

NOVEMBER 15, 2021

Here we briefly describe some of the challenges that data poses to AI. Data annotation. Abundance of data has been one of the main facilitators of the AI boom of the last decade. Deep Learning, a subset of AI algorithms, typically requires large amounts of human annotated data to be useful. Data curation.

Unstructured Data 90

Unstructured Data Recreation/Entertainment Structured Data Reporting 90

CIO Business Intelligence

APRIL 26, 2022

And augmented reality (AR) will enable much more flexibility in the types of skill sets required for certain jobs by pairing remote experts with lower skilled workers wearing cameras and interacting via AR. Ready to evolve your analytics strategy or improve your data quality? How can we affect sustainability in manufacturing?

Manufacturing 87

Manufacturing Optimization Metrics Deep Learning 87

datapine

AUGUST 14, 2019

Exclusive Bonus Content: Download Our Free Data & Science Checklist! Geet our bite-sized free summary and start building your data skills! In the past, data scientists had to rely on powerful computers to manage large volumes of data. Our Top Data Science Tools. Let’s get started.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

DataRobot Blog

MAY 26, 2022

However, with the widespread adoption of modern ML techniques, including gradient-boosted decision trees (GBDTs) and deep learning algorithms , many traditional validation techniques become difficult or impossible to apply.

Risk 52

Risk Modeling Metrics Business Objectives 52

AWS Big Data

MARCH 1, 2023

The inability to accurately find and analyze data from disparate sources represents a potential efficiency killer for everyone from data scientists, medical researchers, academics, to financial and government analysts. We include a code tutorial for you to deploy the resources to run the solution on sample data or your own data.

Data Lake 84

Data Lake Deep Learning Modeling Interactive 84

Smarten

APRIL 19, 2024

The value of an AI-focused analytics solution can only be fully realized when a business has ensured data quality and integration of data sources, so it will be important for businesses to choose an analytics solution and service provider that can help them achieve these goals.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Ontotext

MAY 22, 2023

Luckily, the text analysis that Ontotext does is focused on tasks that require complex domain knowledge and linking of documents to reference data or master data. We use other deep learning techniques for such tasks. Long story short, MDM is mostly about data quality and precision (e.g.,

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

Domino Data Lab

JULY 22, 2019

By virtue of that, if you take those log files of customers interactions, you aggregate them, then you take that aggregated data, run machine learning models on them, you can produce data products that you feed back into your web apps, and then you get this kind of effect in business. You can take TensorFlow.js

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

JUNE 23, 2019

This talk will describe how you can navigate all these challenges that you’re going to face and build a business where every product interaction benefits from your investment in machine learning. It used deep learning to build an automated question answering system and a knowledge base based on that information.

Management 110

Management Machine Learning Experimentation Metrics 110

DataRobot Blog

DECEMBER 20, 2022

You can understand the data and model’s behavior at any time. Once you use a training dataset, and after the Exploratory Data Analysis, DataRobot flags any data quality issues and, if significant issues are spotlighted, will automatically handle them in the modeling stage. Rapid Modeling with DataRobot AutoML.

Management 59

Management Machine Learning Optimization Modeling 59

CIO Business Intelligence

MAY 30, 2024

It has been around since the 1950s with machine learning. Using data and algorithms to imitate the way humans learn came into the scene in the 1980s, and this further evolved to deep learning in the 2000s. Data quality is the cornerstone of effective AI deployment.

Data Quality 82

Data Quality Key Performance Indicator Digital Transformation Management 82

O'Reilly on Data

MARCH 24, 2020

You may picture data scientists building machine learning models all day, but the common trope that they spend 80% of their time on data preparation is closer to the truth. This definition of low-quality data defines quality as a function of how much work is required to get the data into an analysis-ready form.

Machine Learning 352

Machine Learning Statistics Data Quality Modeling 352