O'Reilly on Data

APRIL 19, 2021

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. It’s gratifying to note that organizations starting to realize the importance of data quality (18%).

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

CIO Business Intelligence

JUNE 21, 2022

federal government, HPC is being used to accelerate basic science, develop therapeutics and other treatments for COVID-19, perform military applications such as simulations, handle climate and weather modeling, and a myriad of other tasks in diverse agencies. . Big data analytics is being used to uncover crimes. Within the U.S.

Deep Learning 99

Deep Learning Big Data ROI Software 99

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 81

Metrics Machine Learning Statistics Risk 81

Laminar Security

DECEMBER 13, 2023

Generally, AI algorithms can sift through vast amounts of data at an astonishing speed, identifying patterns and correlations that would be impossible for humans to detect as rapidly and flawlessly. In the context of data discovery, ML algorithms can learn from past data exploration experiences to enhance future ones.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

CIO Business Intelligence

APRIL 25, 2022

The course includes instruction in statistics, machine learning, natural language processing, deep learning, Python, and R. The course culminates in a final data project in collaboration with real-world industry professionals. It culminates with a capstone project that requires creating a machine learning model.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

CIO Business Intelligence

JUNE 1, 2022

These supercomputers power exciting innovations in deep learning, disease control, and physics—think bionic eyes, DNA sequencing for infectious disease research, and the study of time crystals. . CSIRO’s Bracewell Delivers Deep Learning, Bionic Vision. Ready to evolve your analytics strategy or improve your data quality?

Deep Learning 90

Deep Learning Snapshot Optimization Data Quality 90

CIO Business Intelligence

APRIL 25, 2022

But despite all the money flowing into ML projects, most organizations are struggling to get their ML models and applications working on production systems. . And another recent survey has the worst numbers of all, finding that 90% of ML models are not deployed to production. So what’s the problem? The problem with ML.

Machine Learning 129

Machine Learning Enterprise Deep Learning Cost-Benefit 129

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 90

Unstructured Data Deep Learning Insurance Interactive 90

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

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. They opted to run VDI in a private cloud with a pay-as-you-go consumption model. Neither is the directive for employees to be productive and collaborative, nor the needs to protect data and keep costs low.

Technology 57

Technology Cost-Benefit Deep Learning Modeling 57

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. AutoML’s aim is to automate the development of machine learning models.

Analytics 98

Analytics Data Science Modeling Machine Learning 98

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 333

Management Machine Learning Experimentation Metrics 333

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 89

Informatics Modeling Testing Technology 89

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 57

Cost-Benefit Machine Learning Modeling Uncertainty 57

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

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 193

Modeling Machine Learning Testing Metrics 193

CIO Business Intelligence

APRIL 26, 2022

Taking that a step further—with a computer vision platform leveraging a federated approach —the analysis happens in near real time as there would be no need to push large amounts of data back to a central storage location. Then, only the updated model is sent back to the edge. Just starting out with analytics?

Manufacturing 94

Manufacturing Optimization Metrics Deep Learning 94

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

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data.

Enterprise 310

Enterprise Deep Learning Data Governance Risk 310

DataRobot Blog

DECEMBER 15, 2022

AI-powered Time Series Forecasting may be the most powerful aspect of machine learning available today. Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

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 300

Testing Machine Learning Consulting Data Quality 300

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. It must be integrated with business systems to leverage available data.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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. The workflow begins with an AWS Glue job that converts the CSV files into Apache Parquet data format.

Data Lake 85

Data Lake Deep Learning Modeling Interactive 85

Cloudera

OCTOBER 1, 2022

Adam Wood, director of data governance and data quality at a financial services institution (FSI). It is pretty impressive just how much has changed in the enterprise machine learning and AI landscape. For the rest of the organizations though, machine learning and AI were much newer ideas.

Data Governance 75

Data Governance Strategy Enterprise Machine Learning 75

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

Ontotext

MAY 22, 2023

What is the future of knowledge graphs in the era of ChatGPT and Large Language Models? To start with, Large Language Models (LLM) will not replace databases. They are good for compressing information, but one cannot retrieve from such a model the same information that it got trained on. That’s something that LLMs cannot do.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

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

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

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

DataRobot Blog

DECEMBER 20, 2022

Yet, in the digital transformation era, the pricing and assessment of real estate assets is more difficult than described by brokers’ presentations, valuation reports, and traditional analytical approaches like hedonic models. Datarobot enables users to easily combine multiple datasets into a single training dataset for AI modeling.

Management 59

Management Machine Learning Optimization Modeling 59

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 100

Analytics Key Performance Indicator Data Warehouse Data-driven 100

CIO Business Intelligence

APRIL 12, 2022

The challenge is being met using a real-time federated approach with scalable, high-performance hyperconverged infrastructures (HCI), allowing organizations to capture and process large amounts of data at the edge, and provide real-time insights. Ready to evolve your analytics strategy or improve your data quality?

IT 70

IT Cost-Benefit IoT Machine Learning 70

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 88

Data Quality Key Performance Indicator Digital Transformation Management 88

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

CDW Research Hub

SEPTEMBER 15, 2020

They can be inflexible and costly since you are not able to scale your usage as you would using a modern data platform and the cloud. They also aren’t built to integrate new technologies such as artificial intelligence and deep learning tools, which can move business to continuous intelligence and from predictive to prescriptive analytics.

Business Intelligence 52

Business Intelligence Prescriptive Analytics Unstructured Data Data Warehouse 52

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code.

Machine Learning 347

Machine Learning Statistics Data Quality Modeling 347