CIO Business Intelligence

JUNE 1, 2022

The company is applying winning insights from rapid, data-driven, evolutionary models versus relying on engine speed and aerodynamics alone to win races. which are virtual models of objects, systems, or processes ? and artificial intelligence (AI) and machine learning (ML) technologies. .

Manufacturing 105

Manufacturing Digital Transformation Predictive Analytics Strategy 105

CIO Business Intelligence

JUNE 7, 2022

But only in recent years, with the growth of the web, cloud computing, hyperscale data centers, machine learning, neural networks, deep learning, and powerful servers with blazing fast processors, has it been possible for NLP algorithms to thrive in business environments. An Industry Redefining Itself.

Unstructured Data 89

Unstructured Data Deep Learning Insurance Interactive 89

CIO Business Intelligence

APRIL 26, 2022

Ever increasing advances in technology and continuous process optimization techniques have helped ensure that the global supply chain runs efficiently, turning raw materials into products that make their way to physical stores and ecommerce warehouses. Then, only the updated model is sent back to the edge.

Manufacturing 92

Manufacturing Optimization Metrics Deep Learning 92

CIO Business Intelligence

JANUARY 20, 2023

What was once an anomaly is now a global reality as remote and hybrid work models settle in across industries. Emerging from the pandemic, Montage Health leaders aimed to continue and optimize telehealth, telemedicine, virtual care, and virtual visits. They opted to run VDI in a private cloud with a pay-as-you-go consumption model.

Technology 59

Technology Cost-Benefit Deep Learning Modeling 59

CIO Business Intelligence

JUNE 2, 2022

HPC5’s performance level enables sophisticated in-house algorithms to process subsoil data, as well as geophysical and seismic information from around the world. For optimizing existing resources, Eni uses HPC5 to model, study, and ultimately improve refinement operations. .

Informatics 87

Informatics Modeling Testing Technology 87

CIO Business Intelligence

JANUARY 4, 2023

Here are the shifts we noted: Delivery Models : Moving from an almost strictly on-premises systems approach (with some remote access services), HPC is embracing remote delivery models. Of course, data center challenges are also driving demand for these alternatives. Just starting out with analytics?

Cost-Benefit 59

Cost-Benefit Machine Learning Modeling Uncertainty 59

Cloudera

NOVEMBER 15, 2021

Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else. In other words, structured data has a pre-defined data model , whereas unstructured data doesn’t. . The challenges of data. Data annotation. Data curation.

Unstructured Data 91

Unstructured Data Recreation/Entertainment Structured Data Reporting 91

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 332

Management Machine Learning Experimentation Metrics 332

datapine

AUGUST 14, 2019

Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning. Our Top Data Science Tools.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

CIO Business Intelligence

APRIL 4, 2022

Identifying more complex fraud requires a whole new way of looking at data. Traditional relational databases are limited at capturing the relationships between data points. The confines of tabular data models and rigid schemas make it difficult to add new or different kinds of connections.

Technology 52

Technology Deep Learning Cost-Benefit Data Quality 52

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Model development. Model governance. Source: Ben Lorica.

Modeling 194

Modeling Machine Learning Testing Metrics 194

Smarten

APRIL 19, 2024

OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). GPT – Is based on a Large Language Model (LLM). Benefits include customized and optimized models, data, parameters and tuning. Open AI was developed by Microsoft.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 106

Data Lake Unstructured Data Data Warehouse Data Quality 106

DataRobot Blog

DECEMBER 20, 2022

In this context, an augmented intelligence approach around the data will be increasingly more critical for asset managers, investors, and real estate developers to ensure a better understanding of the real estate assets and take better decisions aimed at optimizing both the Net Asset Value and the Net Operating Income.

Management 59

Management Machine Learning Optimization Modeling 59

CIO Business Intelligence

APRIL 12, 2022

Computer vision is helping to reshape the transportation industry at every level from streamlining the passenger experience to preemptive fleet maintenance to fuel optimization. To create a productive, cost-effective analytics strategy that gets results, you need high performance hardware that’s optimized to work with the software you use.

IT 72

IT Cost-Benefit IoT Machine Learning 72

CIO Business Intelligence

MARCH 22, 2022

It’s also hugely beneficial to deploy a data catalog or other centralized management mechanism that automatically discovers, tags, and catalogs data so you can manage and audit your policies all in one place. . Each department or business unit is assigned a “workplace” within the unified data infrastructure.

Analytics 101

Analytics Key Performance Indicator Data Warehouse Data-driven 101

Domino Data Lab

MARCH 3, 2019

The first survey started as a simple exploration into mainstream adoption of machine learning (ML). What’s been the impact of using ML models on culture and organization? Who builds their models? We also used maturity , in other words how long had an enterprise organization been deploying ML models in production?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JUNE 23, 2019

It used deep learning to build an automated question answering system and a knowledge base based on that information. It is like the Google knowledge graph with all those smart, intelligent cards and the ability to create your own cards out of your own data. There’s no substitute for that experience.

Management 110

Management Machine Learning Experimentation Metrics 110

Smart Data Collective

OCTOBER 13, 2021

As we have already said, the challenge for companies is to extract value from data, and to do so it is necessary to have the best visualization tools. Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields).

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135