Hu's Place - HitachiVantara

MAY 10, 2019

Rule-based fraud detection software is being replaced or augmented by machine-learning algorithms that do a better job of recognizing fraud patterns that can be correlated across several data sources. DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective.

Data 90

Data Machine Learning Artificial Inteligence Weak Development Team 90

Apiumhub

APRIL 1, 2021

Learning data science through books will help you get a holistic view of Data Science as data science is not just about computing, it also includes mathematics, probability, statistics, programming, machine learning, and much more. Top Data science books you should definitely read.

Artificial Inteligence 52

Artificial Inteligence Data Handbook Machine Learning 52

Cloudera

JANUARY 20, 2021

The missing chapter is not about point solutions or the maturity journey of use cases, the missing chapter is about the data, it’s always been about the data, and most importantly the journey data weaves from edge to artificial intelligence insight. .

Data 105

Data Artificial Inteligence Analytics Machine Learning 105

Mobilunity

MAY 8, 2023

Analytics zone is where data analysts and data scientists can access the data to perform queries, generate reports, and create models. The analytics zone may include tools for data visualization, machine learning, and predictive analytics.

Engineering 52

Engineering Data Storage AWS 52

Erik Bernhardsson

OCTOBER 27, 2015

I was featured in Peadar Coyle’s interview series interviewing various “data scientists” – which is kind of arguable since (a) all the other ppl in that series are much cooler than me (b) I’m not really a data scientist. So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering.

Data 100

Data Big Data Machine Learning Artificial Inteligence 100

Erik Bernhardsson

OCTOBER 27, 2015

I was featured in Peadar Coyle’s interview series interviewing various “data scientists” – which is kind of arguable since (a) all the other ppl in that series are much cooler than me (b) I’m not really a data scientist. So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering.

Data 100

Data Big Data Machine Learning Artificial Inteligence 100

Trigent

NOVEMBER 3, 2022

Connected logistics devices generate a massive amount of data. The ultimate goal of any organization dealing with a pool of connected devices and sensors is to leverage this data by learning the trends and patterns. There lies the importance of data analytics. Data analytics is not new to us anymore.

Analytics 52

Analytics Data IoT Trends 52

Altexsoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Architecture 52

Architecture Data Storage Machine Learning 52

Mobilunity

DECEMBER 21, 2022

Tech companies use data science to enhance user experience, create personalized recommendation systems, develop innovative solutions, and more. Data science in agriculture can help businesses develop data pipelines specifically for automation and fast scalability. Build and Deploy Machine Learning Models.

Data 52

Data How To Machine Learning Artificial Inteligence 52

Altexsoft

APRIL 13, 2023

“Control towers are the artificial intelligence (AI) of supply chain. Leading executives focus on building resilient and intelligent supply chains that can withstand the turmoil due to data-based proactive decisions. Predictive analytics is about detecting trends and coming up with accurate forecasts of future events.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

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

ParkMyCloud

JULY 30, 2019

One of the nice things about talking with ParkMyCloud users is that we get to see trends often before they are more widely recognized within the industry. The Databricks platform allows enterprises to build their data pipelines across data storage systems and prepare data sets for data scientists and engineers.

Azure 40

Azure AWS Analytics Machine Learning 40

Cloudera

FEBRUARY 19, 2018

Running a large company is impossible without having an ERP system in place, and SAP business software remains at the forefront in this category. But when companies are looking towards new technologies such as data lakes, machine learning or predictive analytics, SAP alone is just not enough.

Data 46

Data Government Big Data Compliance 46

Datavail

JUNE 8, 2023

Data Summit 2023 was filled with thought-provoking sessions and presentations that explored the ever-evolving world of data. From the technical possibilities and challenges of new and emerging technologies to using Big Data for business intelligence, analytics, and other business strategies, this event had something for everyone.

Database Administration 52

Database Administration Data Artificial Inteligence Analytics 52

CIO

AUGUST 18, 2022

Traditionally, organizations have maintained two systems as part of their data strategies: a system of record on which to run their business and a system of insight such as a data warehouse from which to gather business intelligence (BI). You can intuitively query the data from the data lake.

Data 350

Data Artificial Inteligence Technical Advisors Technical Review 350

O'Reilly Media - Ideas

MARCH 6, 2019

Get hands-on training in machine learning, AWS, Kubernetes, Python, Java, and many other topics. Learn new topics and refine your skills with more than 170 new live online training courses we opened up for March and April on the O'Reilly online learning platform. AI and machine learning.

Course 10

Course Artificial Inteligence Training Microservices 10

ParkMyCloud

MAY 29, 2019

Trends in cloud jobs can be overall indicators into trends in the cloud computing space. Here are some trends we’re seeing. Cloud Talent Demand Trends. BI Analyst can also be described as BI Developers, BI Managers, and Big Data Engineer or Data Scientist. Data Detective.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

Altexsoft

MAY 14, 2021

Big Data enjoys the hype around it and for a reason. But the understanding of the essence of Big Data and ways to analyze it is still blurred. This post will draw a full picture of what Big Data analytics is and how it works. Big Data and its main characteristics. Key Big Data characteristics.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Which Big Data tasks does Spark solve most effectively? How does it work?

Big Data 98

Big Data Tools Data Storage 98

BPM

JANUARY 18, 2019

The current Artificial Intelligence (AI) fascination is unfortunately completely biased on Deep Neural Networks (DNN) and Machine Learning (ML) for everything. As we move into a world that is more and more dominated by technologies such as big data, IoT, and ML, more and more processes will be started by external events.

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

Altexsoft

AUGUST 19, 2022

Gartner’s 2022 Magic Quadrant for Analytics and Business Intelligence Platforms. Power BI helps arrange disparate data from multiple sources and make it understandable for end users. For example, the sales teams can scrutinize their turnover and suppliers, run trend analysis, and reveal growth opportunities. per user/month).

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

Abhishek Tiwari

DECEMBER 27, 2017

There are several emerging data trends that will define the future of ETL in 2018. A common theme across all these trends is to remove the complexity by simplifying data management as a whole. For instance, Alluxio, originally known as Tachyon, can potentially use Arrow as its in-memory data structure.

Trends 49

Trends Artificial Inteligence Data Big Data 49

Apiumhub

APRIL 5, 2022

M2- Data Engineering Stage: Technical track focusing on agile approaches to designing, implementing and maintaining a distributed data architecture to support a wide range of tools and frameworks in production. Presentations by some of the leading experts, researchers and practitioners in the area.

Innovation 52

Innovation Data Artificial Inteligence Case Study 52

Altexsoft

SEPTEMBER 9, 2022

In data science , metadata is one of the central aspects: It describes data (including unstructured data streams) fed into a big data analytical platform, capturing, for example, formats, file sizes, source of information, permission details, etc. Types of metadata. There are multiple ways to categorize metadata.

Tools 59

Tools Technical Review Software Review Systems Review 59

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

Existek

JANUARY 11, 2022

Being a market leader, AWS continues bringing new trends and approaches. For example, they considerably revised the cloud strategy due to the need to transform the delivery model from PaaS to IaaS, thus renaming Windows Azure to Microsoft Azure in 2014. . Machine learning. Development Operations Engineer $122 000.

Azure 52

Azure AWS Cloud How To 52

Altexsoft

FEBRUARY 8, 2021

And planning, in turn, relies on understanding of current performance, past trends, existing risks, and possible future scenarios. To support the planning process, predictive analytics and machine learning (ML) techniques can be implemented. Comparison between traditional and machine learning approaches to demand forecasting.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Cloudera

DECEMBER 14, 2020

If there’s one thing enterprises have learned in 2020, it’s how to navigate through uncertain times, and in 2021, organizations will likely have to continue navigating through a shifting landscape. Today, a new modern data platform is here to transform how businesses take advantage of real-time analytics.

Analytics 69

Analytics Big Data Government Cloud 69

Altexsoft

FEBRUARY 22, 2022

Predictive maintenance (PdM) involves constant monitoring of your equipment condition and conducting repairs only when bad trends are detected – but before breakdowns occur. Reporting and analytics is essential to obtain a bird eye view of your fleet and make data-based decisions. Processing data.

Software Review 52

Software Review Technical Review Software Technology 52

Altexsoft

AUGUST 22, 2022

Gartner claims that there is such a solution capable of enabling companies to have advanced, flexible, and reusable data management across all environments. The solution is called a data fabric. Remember that a data fabric is NOT a single software tool you can buy and deploy overnight. Consolidated data protection.

Architecture 52

Architecture Artificial Inteligence Technical Review Data 52

Exadel

AUGUST 26, 2020

As MastersInDataScience.org explains, data analytics is a broad term including the following subtypes: descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. All of these types of data analytics rely on data warehousing and machine learning. Customized visualization.

Analytics 52

Analytics Data How To Machine Learning 52

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.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Cloudera

NOVEMBER 2, 2020

Correlations across data domains, even if they are not traditionally stored together (e.g. real-time customer event data alongside CRM data; network sensor data alongside marketing campaign management data). The extreme scale of “big data”, but with the feel and semantics of “small data”.

Data 91

Data Analytics Storage Big Data 91

O'Reilly Media - Ideas

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

Conference 8

Conference Data Data Engineering Artificial Inteligence 8

Cloudera

FEBRUARY 11, 2019

To achieve their goals of digital transformation and becoming data-driven, companies need more than just a better data warehouse or BI tool. They need a range of analytical capabilities from data engineering to data warehousing to operational databases and data science. Governing machine learning.

Government 57

Government Compliance Machine Learning Artificial Inteligence 57

Altexsoft

OCTOBER 26, 2022

To enable this conversion, a CDO uses digital information and modern technologies such as the cloud, the Internet of Things , mobile apps, social media, machine learning-based products, and digital marketing. The CDO always has his eye on the next new digital trend and decides whether it’s worth a company’s attention.

CTO 52

CTO Organization Technical Review Culture 52

Cloudera

AUGUST 24, 2020

Today we are continuing our discussion with Martin Mannion , EMEA Big Data Community lead at Deloitte and Paul Mackay, the EMEA Cloud Lead at Cloudera to look at why security and governance requirements must be tackled in the early stages of data-led use case development, thereby mitigating more work later on.

Cloud 76

Cloud Data Government Innovation 76