OpenCredo

JULY 31, 2018

Machine Learning, alongside a mature Data Science, will help to bring IT and business closer together. By leveraging data for actionable insights, IT will increasingly drive business value. The Role of Data. The reason for this is the central role that data plays in machine learning.

Artificial Inteligence 40

Artificial Inteligence Machine Learning Data Artificial Intelligence 40

Kentik

APRIL 12, 2023

Data scientists play a critical role in the DataOps ecosystem, leveraging advanced analytics and machine learning techniques to gain insights from large and complex data sets. DataOps team roles In a DataOps team, several key roles work together to ensure the data pipeline is efficient, reliable, and scalable.

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

Hu's Place - HitachiVantara

JANUARY 3, 2019

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Building an AI or machine learning model is not a one-time effort.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

Capgemini

APRIL 20, 2022

This article first appeared on Capgemini’s Data-powered Innovation Review | Wave 3. In today’s data-driven economy, artificial intelligence (AI) and machine learning (ML) are powering digital transformation in every industry around the world. Accelerate engineering. Boost efficiency. Informatica.

Artificial Inteligence 98

Artificial Inteligence Data Systems Review Technical Review 98

Cloudera

DECEMBER 20, 2022

McKinsey estimates that the use of data-driven technologies can drive operating and maintenance cost savings of more than 12%. For example, predictive maintenance, based on machine learning, will enable utility companies to take preventative action that avoids large-scale power outages and costs.

Energy 81

Energy Analytics Data Machine Learning 81

Cloudera

JUNE 7, 2022

Only Cloudera has the power to span multi-cloud and on-premises with a hybrid data platform. We deliver cloud-native data analytics across the full data lifecycle – data distribution, data engineering, data warehousing, transactional data, streaming data, data science, and machine learning – that’s portable across infrastructures.

Data 112

Data Architecture Analytics Government 112

Trigent

NOVEMBER 3, 2022

AI and machine learning are already playing a significant role in shaping new initiatives for the logistics industry. With data analytics at the helm, organizations are now bringing their predictive and prescriptive learnings to the fore while harvesting descriptive data to get a competitive edge.

Analytics 52

Analytics Data IoT Trends 52

Altexsoft

JUNE 26, 2023

While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Coforge

AUGUST 31, 2020

The drastic shift from traditional and orthodox system frameworks and operations can be unarguably attributed to the influx of intelligent technologies such as Artificial Intelligence, Machine Learning, Big Data, Blockchain/DLT led smart contracts, and widespread usage of cloud systems. .

Insurance 40

Insurance Technology Artificial Inteligence Policies 40

Altexsoft

NOVEMBER 9, 2021

Traditional statistical methods use mainly internal, historical data to predict trends within relatively stable markets. Meanwhile, machine learning (ML) techniques are capable of processing a wide range of both historical and current data from multiple external and internal sources. Extract data. Consolidate data.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Cloudera

MARCH 6, 2018

The real power in machine learning and analytics is when multiple analytics disciplines are able to work together in concert, sharing data in service of solving more complex and more valuable questions. This clearly defines the data context even for transient compute applications. Soon we’ll have Altus Data Science, too!

Analytics 40

Analytics Cloud Big Data AWS 40

TIBCO - Connected Intelligence

NOVEMBER 13, 2019

TIBCO’s analytics and data management solutions help companies speed time to insight from data, and drive actions that impact the business. When we interviewed Ravi Hubbly, co-founder and president, explained that “healthcare has a lot of potential ways to become much more efficient and much more effective.”.

Data 4

Data Artificial Inteligence Analytics Healthcare 4

Cloudera

OCTOBER 11, 2021

Modak, a leading provider of modern data engineering solutions, is now a certified solution partner with Cloudera. Customers can now seamlessly automate migration to Cloudera’s Hybrid Data Platform — Cloudera Data Platform (CDP) to dynamically auto-scale cloud services with Cloudera Data Engineering (CDE) integration with Modak Nabu.

Data Engineering 95

Data Engineering Engineering Data Cloud 95

Cloudera

APRIL 1, 2024

Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics. Our customers run some of the world’s most innovative, largest, and most demanding data science, data engineering, analytics, and AI use cases, including PB-size generative AI workloads.

Artificial Inteligence 105

Artificial Inteligence Cloud Generative AI Analytics 105

Cloudera

MAY 18, 2021

Operational efficiency and risk reduction by automating most of the manual upgrade and configuration tasks being implemented manually in IaaS deployments. that optimizes autoscaling for compute resources compared to the efficiency of VM-based scaling. . Machine Learning Prototypes. Operational efficiency.

Cloud 86

Cloud Technical Review Storage Backup 86

Cloudera

FEBRUARY 22, 2021

The valuation framework consists of four dimensions: 1) business value acceleration, 2) technology cost reduction and / or avoidance, 3) infrastructure cost optimization and 4) operational efficiency. Operational efficiency. In the following sections I present the approach for quantifying each of these dimensions. .

Data 82

Data Analytics Government Technical Review 82

Cloudera

NOVEMBER 22, 2021

In addition, data pipelines include more and more stages, thus making it difficult for data engineers to compile, manage, and troubleshoot those analytical workloads. different analytical frameworks) for complex use cases that span different stages across the data lifecycle? Operational Scalability. CRM platforms).

Scalability 94

Scalability Technical Review Data Analytics 94