Cloudera

AUGUST 31, 2018

We learned a lot about data center automation based on real-time application and diagnostic feedback using applied machine learning. Witnessing these challenges, we focused on solving them through machine learning applied to workload and cluster optimization.

Machine Learning 70

Machine Learning Artificial Inteligence Analytics Engineering Culture 70

Cloudera

APRIL 9, 2021

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines. Here are the key stages: .

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CIO

OCTOBER 13, 2023

In the next six to 12 months, some of the most popular anticipated uses for gen AI include content creation (42%), data analytics (53%), software development (41%), business insight (51%), internal customer support (45%), product development (40%), security (42%), and process automation (51%).

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Perficient

MARCH 5, 2024

Today, we’re diving headfirst into the world of advanced reporting and analytics in Salesforce, with a special focus on leveraging Einstein Analytics to unlock the full potential of your data. Introducing Einstein Analytics So, what exactly is Einstein Analytics?

Analytics 59

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Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

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Datavail

MARCH 8, 2022

were unsuccessful in fulfilling their aspirations of implementing Machine Learning (ML) systems in 2021. Just as there’s no end to the number of potential business challenges every organization faces, neither is there an end to the analytic possibilities offered by unique ML models. There’s No Reason Not To.

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Dataiku

NOVEMBER 4, 2020

The bulk of an organization’s data science, machine learning, and AI conquests come down to improving decision-making capabilities. Is there a time when both analytics approaches should be used in unison? We’ll unpack the answers in this blog post.

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Cloudera

OCTOBER 14, 2020

To maximize safety for all passengers and crew members, while also delivering profits, airlines have heavily invested in predictive analytics to gain insight on the most cost-effective way to maintain real-time engine performance. The Process. Airlines design their aircraft to operate at 99.999% reliability. Conclusion.

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Artificial Inteligence Machine Learning Engineering Airlines 98

DevOps.com

MAY 27, 2019

DevOps teams seeking to step up their mojo in developing cutting-edge artificial intelligence (AI) features are facing a big skills bottleneck when it comes to data analytics and machine learning modeling. The post Are Self-Service Machine Learning Models the Future of AI Integration?

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Cloudera

SEPTEMBER 22, 2022

To me, this means that by applying more data, analytics, and machine learning to reduce manual efforts helps you work smarter. Simply stated, this approach enables data to be collected from any location and reside in any location for analytics to then be performed. Step two: expand machine learning and AI.

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DevOps.com

FEBRUARY 5, 2019

OverOps is adding a series of dashboards to its analytics platform that employ machine learning algorithms to identify which software application anomalies in a pre-production environment are likely to have the most impact on a production environment.

Machine Learning 94

Machine Learning Artificial Inteligence Applications DevOps 94

Coforge

AUGUST 27, 2020

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Cloudera

JANUARY 6, 2021

Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. In this blog series, we will explain how to configure PySpark and HBase together for basic Spark use as well as for jobs maintained in CDSW. Example Operations .

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Cloudera

AUGUST 17, 2021

Dear Parents and Educators and Friends of Cloudera, If you are reading this blog, you know us at Cloudera as a group of self-described data geeks and data analysts. Our company mission is to make data and analytics easy and accessible, for everyone. However, data doesn’t just make a difference for enterprises. And we mean it.

Machine Learning 124

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Cloudera

JULY 18, 2018

Despite advances made in EHRs of late, they, unfortunately, do not provide advanced analytics or intelligent search for that matter. It is fair to say that healthcare faces many challenges, including developing, deploying, and integrating machine learning and artificial intelligence (AI) into clinical workflow and care delivery.

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Artificial Inteligence Machine Learning Analytics Healthcare 53

CTOvision

NOVEMBER 6, 2018

Read Zayan Guedim explain how a new hybrid system combines classical machine learning techniques with quantum computation to optimize the management of the power grid on Edgylabs blog : Strategically deployed across the grid, Phasor Measurement Units (PMUs) are sensor devices that measure currents and voltages at a particular time.

Machine Learning 116

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Cloudera

OCTOBER 30, 2023

The public cloud is increasingly becoming the preferred platform to host data analytics – related projects, such as business intelligence, machine learning (ML), and AI applications. Cloudera FinOps Capabilities CDP is a cloud-native platform helping companies accelerate cloud adoption to run their data and analytics workloads.

Analytics 82

Analytics Cloud Technical Review Resources 82

InfoBest

SEPTEMBER 19, 2021

Machine learning development. In the case of companies looking to improve their workflows and to become more digital it is usually machine learning development, a branch of A.I. Machine learning development, compared to more classic A.I., Machine learning development, compared to more classic A.I.,

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Apps Associates

DECEMBER 9, 2020

Introduction: In our previous blog, OAC Machine Learning we learned about identifying, loading and preparing data using two different data sources in order to achieve the objective of our model. Part 1: OAC Machine Learning – Let’s Get Started & Data Preparation. Exploratory Data Analysis.

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N2Growth Blog

APRIL 19, 2024

By utilizing machine learning to streamline processes and leveraging data analytics to gain a deeper understanding of customer behavior, digital tools provide innovative solutions to today’s economic challenges. It is the driving force behind the shift from traditional brick-and-mortar businesses to the virtual world.

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Cloudera

DECEMBER 20, 2022

In this respect, several studies project that a proper use of advanced analytics implies savings of between 5% and 7.5%. 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 84

Energy Analytics Data Machine Learning 84

Cloudera

OCTOBER 13, 2021

Imagine if you had to explain what machine learning is and how to use it. Cloudera produced a series of ebooks — Production Machine Learning For Dummies , Apache NiFi For Dummies , and Apache Flink For Dummies (coming soon) — to help simplify even the most complex tech topics. There’s no need to panic.

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Apps Associates

DECEMBER 3, 2020

This is the first installment of three-part blog series which is divided into these three segments: Part 1: OAC Machine Learning – Let’s Get Started & Data Preparation. Part 2: OAC Machine Learning – Gain Deeper Insights Through Visual Exploration. Preparing the data for Machine Learning in OAC.

Machine Learning 40

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Apps Associates

DECEMBER 9, 2020

Introduction: In our previous blog, OAC Machine Learning we learned about identifying, loading and preparing data using two different data sources in order to achieve the objective of our model. In this blog we will gain … Continue reading "Gaining Deeper Insights of Data with Oracle OAC Machine Learning".

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Apps Associates

DECEMBER 10, 2020

Introduction: In our previous blog, we were able to gain deeper insights into our data through exploratory data analysis and as a supplement we were also able to create a Data Visualization project with graphs generated from the Explain feature.

Machine Learning 52

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CIO

FEBRUARY 14, 2023

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. AI continues to transform customer engagements and interactions with chatbots that use predictive analytics for real-time conversations.

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UruIT

AUGUST 4, 2021

Technologies such as data analytics, artificial intelligence, and machine learning can provide a huge help to healthcare institutions. . Read this article to discover five exceptional ways in which machine learning can support your digital health product. . Connecting Patients With Doctors.

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Artificial Inteligence Machine Learning Healthcare Examples 52

Altexsoft

SEPTEMBER 19, 2023

Beyond its fundamental role in elevating customer experience, travel analytics has a valuable impact on revenue growth and cultivating a competitive edge. This article explores the influence of analytics on marketing strategies, revenue management, guest personalization, and other aspects of the travel industry.

Travel 52

Travel Analytics Examples Airlines 52

Datavail

MARCH 3, 2022

Today’s advanced technologies provide data analytics programming to understand, learn from, and harness the values hidden deep in those data center depths. The proliferation of data analytics programming is advancing industries and economies, driven by cloud computing and the ever-widening expansion of digital connectedness.

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DXC

OCTOBER 6, 2020

Up until recently this has meant hiring data scientists who know how to create machine-learning algorithms, build predictive computing models, integrate data from multiple sources (including unstructured data), and uncover […].

Analytics 80

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Openxcell

JUNE 9, 2023

Big Data analytics,on the other hand, refers to using this huge amount of data to make informed business decisions. Let us have a look at Big Data Analytics more in detail. What is Big Data Analytics? This blog explains all about Big Data Analytics, its trends, tools and more.

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Cloudera

AUGUST 3, 2021

Advanced analytics empower risk reduction . Advanced analytics and enterprise data are empowering several overarching initiatives in supply chain risk reduction – improved visibility and transparency into all aspects of the supply chain balanced with data governance and security. . Improve Visibility within Supply Chains.

Analytics 111

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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.

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Cloudera

SEPTEMBER 15, 2021

Deploying machine learning (ML) and analytics capabilities at the edge is what makes this possible. . Real-time analytics isn’t always about life-and-death situations, though. The algorithms are imperfect, but they will get better in time as they “learn” by processing more data. . Fast-changing Data.

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OTS Solutions

AUGUST 16, 2023

Artificial Intelligence (AI) and Machine Learning (ML) have been at the forefront of app modernization, helping businesses to streamline workflows, enhance user experience, and improve app security measures. AI and ML are transforming the way applications are developed and optimized.

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TechCrunch

FEBRUARY 26, 2021

Atlassian launched a machine learning layer, which relies on data on the platform with the addition of Atlassian Smarts last fall. Chartio co-founder and CEO Dave Fowler wrote in a blog post on his company website that the two companies started discussing a deal late last year, which culminated in today’s announcement.

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Apiumhub

APRIL 6, 2021

There are a lot of excellent blogs on data science, and it can be a little overwhelming to know where to start. This article will list some of the most useful blogs for everyone – from data science experts to complete newbies. Top Data Science Blogs to follow.

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